Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning | Paper Citation Record

Observation e7ae3943-dd81-4e4f-a762-677cde8d6854 · outbound

This paper cites Dense semantic contrast for self-supervised visual representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Dense semantic contrast for self-supervised visual representation learning,

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This paper cites Unsupervised learning of dense visual representa- tions,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unsupervised learning of dense visual representa- tions,

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This paper cites Densecl: A simple framework for self-supervised dense visual pre-training,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Densecl: A simple framework for self-supervised dense visual pre-training,

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This paper cites Exploring set similarity for dense self-supervised representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Exploring set similarity for dense self-supervised representation learning,

Reference 4

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This paper cites Propa- gate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Propa- gate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,

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This paper cites Representation learning: A review and new perspectives,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Representation learning: A review and new perspectives,

Reference 6

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This paper cites Sim- cvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Sim- cvd: Simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation,

Reference 7

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This paper cites Learning better registration to learn better few- shot medical image segmentation: Authenticity, diversity, and robustness,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Learning better registration to learn better few- shot medical image segmentation: Authenticity, diversity, and robustness,

Reference 8

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Observation cda6c5bd-a3ac-432c-a342-9d3d5e79e9d8 · outbound

This paper cites Meta grayscale adaptive network for 3d integrated renal structures segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Meta grayscale adaptive network for 3d integrated renal structures segmentation,

Reference 9

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Observation a5fab287-19b2-478b-a0ba-1c5a5f4eac68 · outbound

This paper cites JOURNAL OF LATEX CLASS FILES, VOL.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning JOURNAL OF LATEX CLASS FILES, VOL

Reference 10

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Observation f3e81327-2181-4691-8a90-01657e4ee44c · outbound

This paper cites Class-aware adversarial transformers for medi- cal image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Class-aware adversarial transformers for medi- cal image segmentation,

Reference 11

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This paper cites A survey on deep learning in medicine: Why, how and when?.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning A survey on deep learning in medicine: Why, how and when?

Reference 12

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Observation adaadd3a-68cb-4283-b889-931d17e4dc2f · outbound

This paper cites Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learn- ing in medical image analysis,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learn- ing in medical image analysis,

Reference 13

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Observation 4e445a19-b293-45d1-a8ca-6fd0f2942633 · outbound

This paper cites Visual similarity and representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Visual similarity and representation learning,

Reference 14

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Observation 6a849f24-6068-4a21-b01d-c07da5c62640 · outbound

This paper cites Attributable visual similarity learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Attributable visual similarity learning,

Reference 15

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Observation 8d4a2fe6-7056-47ca-a73b-70ca9b00795a · outbound

This paper cites Pads: Policy-adapted sampling for visual similarity learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Pads: Policy-adapted sampling for visual similarity learning,

Reference 16

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Observation 5365fdd1-1f92-438d-baf2-b6839dcf234c · outbound

This paper cites Momentum con- trastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Momentum con- trastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation,

Reference 17

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Observation cef26e52-e945-4898-8f6e-12f3b2463616 · outbound

This paper cites A survey on deep learning in medical image analysis,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning A survey on deep learning in medical image analysis,

Reference 18

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Observation eb5c7ffc-2097-4c34-8cc8-cccda36c990e · outbound

This paper cites Debiased contrastive learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Debiased contrastive learning,

Reference 19

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This paper cites Robust contrastive learning against noisy views,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Robust contrastive learning against noisy views,

Reference 20

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Observation ef5fd5ba-9b52-41ac-84cd-48e6f4e59b32 · outbound

This paper cites High- resolution encoder–decoder networks for low-contrast medi- cal image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning High- resolution encoder–decoder networks for low-contrast medi- cal image segmentation,

Reference 21

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This paper cites Momentum contrast for unsupervised visual representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Momentum contrast for unsupervised visual representation learning,

Reference 22

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Observation e49d1725-1c76-4d3c-9a74-85677f6a999f · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning A simple framework for contrastive learning of visual representations,

Reference 23

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Observation 32a521bb-c4c6-4eaa-96da-b08b54b93590 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Improved Baselines with Momentum Contrastive Learning

Reference 24

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This paper cites Bootstrap your own latent: A new approach to self-supervised learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Bootstrap your own latent: A new approach to self-supervised learning,

Reference 25

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Observation 4d1509f9-270c-4126-a7e2-00e6ecf91683 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Momentum contrast for unsupervised visual representation learning,

Reference 26

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Observation e7976cfd-ed7f-48d2-8988-180ea78a2fb3 · outbound

This paper cites Alexandroff and H.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Alexandroff and H

Reference 27

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Observation d0457c93-c389-4129-a8fe-4c6850b79a48 · outbound

This paper cites A new privacy homomorphism and applications,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning A new privacy homomorphism and applications,

Reference 28

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Observation 86cbc644-8f68-436a-872e-2464ee1af5f7 · outbound

This paper cites Statistical shape models for 3d medical image segmentation: a review,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Statistical shape models for 3d medical image segmentation: a review,

Reference 29

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Observation 3a257301-1f16-4740-9f3e-1133109a4187 · outbound

This paper cites Homeomorphic brain image seg- mentation with topological and statistical atlases,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Homeomorphic brain image seg- mentation with topological and statistical atlases,

Reference 30

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This paper cites Group actions, homeomorphisms, and matching: A general framework,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Group actions, homeomorphisms, and matching: A general framework,

Reference 31

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This paper cites an unresolved cited work.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unresolved cited work

Reference 32

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This paper cites an unresolved cited work.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unresolved cited work

Reference 33

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Observation 915441ba-6930-419e-b7ef-c7f3ff77165e · outbound

This paper cites Geometric visual similarity learning in 3d medical image self-supervised pre-training,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Geometric visual similarity learning in 3d medical image self-supervised pre-training,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.633302Z

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Observation 2759e697-45cd-4c16-85db-41d8e0c5ab18 · outbound

This paper cites Collocation for diffeomorphic deformations in medical image registration,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Collocation for diffeomorphic deformations in medical image registration,

Reference 35

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Observation eeb61b0a-f2ea-446a-96d5-7a90d3eadfe4 · outbound

This paper cites An unsupervised learning model for deformable med- ical image registration,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning An unsupervised learning model for deformable med- ical image registration,

Reference 36

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Observation 0e18d7a9-1c38-49dd-9e1a-f6481108e433 · outbound

This paper cites Deep learning in medical image registration: a survey,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deep learning in medical image registration: a survey,

Reference 37

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Observation 75f92bee-81bb-403f-beb9-c35a42e95686 · outbound

This paper cites Un- supervised learning of probabilistic diffeomorphic registration for images and surfaces,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Un- supervised learning of probabilistic diffeomorphic registration for images and surfaces,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.577503Z

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Observation b9afd9d0-a677-4633-9302-941215135b99 · outbound

This paper cites The correspondence prob- lem,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning The correspondence prob- lem,

Reference 39

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Observation 5fdf03ea-b302-45a9-ab0f-72357e757178 · outbound

This paper cites Imitation: is cognitive neuroscience solving the correspondence problem?.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Imitation: is cognitive neuroscience solving the correspondence problem?

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.548006Z

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Observation a9364978-841e-4f5d-a809-3c1212a00d2b · outbound

This paper cites Object correspondence as a machine learning problem,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Object correspondence as a machine learning problem,

Reference 41

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Observation 2fe8164a-731e-47c4-9938-faf8b57f35d6 · outbound

This paper cites Few-shot learning for deformable medical image regis- tration with perception-correspondence decoupling and reverse teaching,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Few-shot learning for deformable medical image regis- tration with perception-correspondence decoupling and reverse teaching,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.519940Z

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This paper cites Human- level concept learning through probabilistic program induction,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Human- level concept learning through probabilistic program induction,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.506175Z

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Observation 155ea930-f7e6-4004-a517-7f7ee5af66c7 · outbound

This paper cites Highly accurate protein structure prediction with al- phafold,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Highly accurate protein structure prediction with al- phafold,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.492516Z

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Observation 6d8e7c3e-cfc7-457d-a147-dd9e112acc70 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 45

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Observation 5a12a8d3-a4e5-43a8-85a4-63773548b903 · outbound

This paper cites Unsupervised learning of local discriminative representation for medical images,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unsupervised learning of local discriminative representation for medical images,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.478880Z

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Observation ec2c09d3-ff57-4438-8a66-48f8f577d841 · outbound

This paper cites Unsupervised representation learning for tissue segmentation in histopathological images: From global to local contrast,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unsupervised representation learning for tissue segmentation in histopathological images: From global to local contrast,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.463374Z

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Observation f9034c8c-1a98-480b-9e62-7e9d2aeae837 · outbound

This paper cites The mahalanobis distance,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning The mahalanobis distance,

Reference 48

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Observation b1e85b69-6f22-42ec-b837-0ae89f3243e9 · outbound

This paper cites On the euclidean distance of im- ages,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning On the euclidean distance of im- ages,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.436484Z

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Observation d0bf65da-6f4a-449f-aac6-b342f80ded46 · outbound

This paper cites Contrastive learning of global and local features for medical image segmen- tation with limited annotations,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Contrastive learning of global and local features for medical image segmen- tation with limited annotations,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.422667Z

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Observation 47170e36-35e0-473d-b433-738d790b02f4 · outbound

This paper cites Exploring simple siamese representation learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Exploring simple siamese representation learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.408340Z

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Observation eeb34686-bfe3-43b6-8f38-911732657da4 · outbound

This paper cites Deep clus- tering for unsupervised learning of visual features,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deep clus- tering for unsupervised learning of visual features,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.394416Z

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Observation f98bf4e2-3e1d-464e-a643-2d7ebe606005 · outbound

This paper cites Understanding di- mensional collapse in contrastive self-supervised learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Understanding di- mensional collapse in contrastive self-supervised learning,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.379185Z

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Observation 81c85ad7-9697-4d83-9d51-3823b829e6ff · outbound

This paper cites Boosting contrastive self-supervised learning with false nega- tive cancellation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Boosting contrastive self-supervised learning with false nega- tive cancellation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.363571Z

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Observation c9d1b3bd-218a-4210-b857-677a2694179c · outbound

This paper cites Multi-atlas segmentation with joint label fusion,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Multi-atlas segmentation with joint label fusion,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.348235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09c6f7c0-f157-4c1c-92c2-c775234fa814 · outbound

This paper cites Multi-atlas segmentation using partially annotated data: Methods and an- notation strategies,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Multi-atlas segmentation using partially annotated data: Methods and an- notation strategies,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.334172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2c237ba0-8b7a-4628-9560-d634b1bccd11 · outbound

This paper cites Lt-net: Label transfer by learning reversible voxel- wise correspondence for one-shot medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Lt-net: Label transfer by learning reversible voxel- wise correspondence for one-shot medical image segmentation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.319572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf58c253-b60b-40b2-a721-1c592835cbdc · outbound

This paper cites Voxelmorph: A learning framework for deformable med- ical image registration,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Voxelmorph: A learning framework for deformable med- ical image registration,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.305244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1174f8f2-cc21-4d33-acd6-1e099c253b37 · outbound

This paper cites Deep learning,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deep learning,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.320119Z

Source-reported events for the cited work

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Observation 08fac1c6-1f81-46f3-a602-404e75ee4a57 · outbound

This paper cites Deep learning in medical image analysis,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deep learning in medical image analysis,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.281813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0703ccc8-d529-432b-924f-0b40310c9552 · outbound

This paper cites Deep complementary joint model for complex scene registration and few-shot segmentation on medi- cal images,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deep complementary joint model for complex scene registration and few-shot segmentation on medi- cal images,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.268120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b09c0b7b-b81a-4e4e-8180-4e154fc3ef2d · outbound

This paper cites Modeling the probabilistic distribu- tion of unlabeled data for one-shot medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Modeling the probabilistic distribu- tion of unlabeled data for one-shot medical image segmentation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.254657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8fabd5e4-864a-466c-b87a-97770d09ec60 · outbound

This paper cites Data augmentation using learned transformations for one-shot medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Data augmentation using learned transformations for one-shot medical image segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.241104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a899f60c-fa34-4402-9dc3-d78d272cdfa2 · outbound

This paper cites Deepatlas: Joint semi-supervised learning of image registration and segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Deepatlas: Joint semi-supervised learning of image registration and segmentation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.227428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8af26c6-1566-4397-bace-d51d18d5d735 · outbound

This paper cites Digital topology: Introduction and survey,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Digital topology: Introduction and survey,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.213484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c575a238-f4f7-49d5-9712-716b1eaee5a4 · outbound

This paper cites Large-scale machine learning with stochastic gradient descent,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Large-scale machine learning with stochastic gradient descent,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.199800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68275a65-0f03-44ca-8eed-3f00db9c5091 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Adam: A Method for Stochastic Optimization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.354735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1fe231e-74e3-4e5d-96a7-96a426e02624 · outbound

This paper cites Coalitional game theory for communication networks,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Coalitional game theory for communication networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.185602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cfeab220-394f-4451-a72b-24e49a4f1268 · outbound

This paper cites Models genesis,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Models genesis,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.171963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6fdd8cf1-4540-43ca-9387-8a70bc13edba · outbound

This paper cites Context encoders: Feature learning by inpainting,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Context encoders: Feature learning by inpainting,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.158670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3e99e250-6945-4a2d-bcc0-bd4584d40e83 · outbound

This paper cites Loss odyssey in medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Loss odyssey in medical image segmentation,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.145460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 163fced3-a278-4243-8d08-48d9256a08b1 · outbound

This paper cites Automated segmentation of normal and diseased coronary arteries-the asoca challenge,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Automated segmentation of normal and diseased coronary arteries-the asoca challenge,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.131819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d93c8620-f4ec-4255-b69e-d2c3829af612 · outbound

This paper cites Standardized evaluation methodology and refer- ence database for evaluating coronary artery centerline extraction algorithms,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Standardized evaluation methodology and refer- ence database for evaluating coronary artery centerline extraction algorithms,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.117321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df9e9442-ad04-42ac-90bb-24a6549faf46 · outbound

This paper cites Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.103355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 96718efb-3da0-464d-b5de-5468dcf85425 · outbound

This paper cites Candishare: A resource for pediatric neu- roimaging data,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Candishare: A resource for pediatric neu- roimaging data,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.090128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8bd9b8c4-d303-4de5-a6bc-d92a46324f06 · outbound

This paper cites Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.076864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b662a616-6543-448f-a139-338c1ffc1404 · outbound

This paper cites Chestx-ray8: Hospital-scale chest x-ray database and bench- marks on weakly-supervised classification and localization of common thorax diseases,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Chestx-ray8: Hospital-scale chest x-ray database and bench- marks on weakly-supervised classification and localization of common thorax diseases,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.061575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 570a3257-6eed-49b2-91da-98637733f101 · outbound

This paper cites Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Mine your own anatomy: Revisiting medical image segmentation with extremely limited labels,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.046446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da81f17b-7dda-492f-9b2d-e1a1164db771 · outbound

This paper cites Rethinking semi-supervised medical image seg- mentation: A variance-reduction perspective,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Rethinking semi-supervised medical image seg- mentation: A variance-reduction perspective,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.032459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c66ba897-6a72-41f0-97f3-dbaa7da11d8b · outbound

This paper cites Shiraishi, S.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Shiraishi, S

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.017805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d97c6b98-8d45-4fca-b5c2-83c15db4f756 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning U-net: Convolutional networks for biomedical image segmentation,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:21.003277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78ecc5a8-3c4e-411e-b306-68cc1e46d69b · outbound

This paper cites Uncertainty- aware self-ensembling model for semi-supervised 3d left atrium segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Uncertainty- aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.987140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e58f002e-c91e-4192-ad94-4cffc3fb2ed3 · outbound

This paper cites Multi-task attention-based semi-supervised learning for medical image segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Multi-task attention-based semi-supervised learning for medical image segmentation,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.972035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5aa4948a-8907-489b-98fd-6b9278ce534a · outbound

This paper cites Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Dense biased networks with deep priori anatomy and hard region adaptation: Semi- supervised learning for fine renal artery segmentation,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.956950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 83dccf50-1897-4830-904a-6e6a109bc06c · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Semi-supervised semantic segmentation with cross pseudo supervision,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.942190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2fba5671-d007-4d78-a43b-bbace26df290 · outbound

This paper cites Group normalization,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Group normalization,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.927904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b027cb38-d497-4c29-89c8-cef2637f72af · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Pytorch: An imperative style, high-performance deep learning library,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.442516Z

Source-reported events for the cited work

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Observation 69a65605-5b3f-41bb-9c2a-0815d00ae276 · outbound

This paper cites Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.903857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df360aa3-727a-419a-a126-c8bdd7d76b83 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Imagenet: A large-scale hierarchical image database,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.451124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119701c8-d69a-44f1-87c5-d89770a5e5ac · outbound

This paper cites Stacked denoising autoencoders: Learning useful rep- resentations in a deep network with a local denoising criterion.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Stacked denoising autoencoders: Learning useful rep- resentations in a deep network with a local denoising criterion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.880539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3008ee53-2598-4b76-bd77-937ebb73da00 · outbound

This paper cites Unsupervised representation learning by predicting image rotations,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Unsupervised representation learning by predicting image rotations,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.865475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8221583d-74a8-40f3-9fa1-4185da8c72bf · outbound

This paper cites Big data application in biomedical research and health care: a literature review,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Big data application in biomedical research and health care: a literature review,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.849991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 50863a0d-62ca-4998-a4ff-ee5c586ddb50 · outbound

This paper cites Superpixel segmenta- tion with fully convolutional networks,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Superpixel segmenta- tion with fully convolutional networks,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.836021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 140f7141-84c2-42b2-b525-5cdc5ca71aa2 · outbound

This paper cites Imaging foundation model for universal enhancement of non-ideal mea- surement ct,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Imaging foundation model for universal enhancement of non-ideal mea- surement ct,

Reference 94

Resolution: verified exact; raw_fallback, observed 2026-08-08T19:58:20.680258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f6f75b79-8569-4ba5-bb5f-5e1b06b789ca · outbound

This paper cites Foundation model for advancing healthcare: Challenges, op- portunities and future directions,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Foundation model for advancing healthcare: Challenges, op- portunities and future directions,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.822031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7741eb18-9d87-4d0c-800c-45e01d6dab76 · outbound

This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.481156Z

Source-reported events for the cited work

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Observation 0d5103fe-a9da-47b8-9251-f32b2a0f0f32 · outbound

This paper cites Automated brain extraction of multisequence mri using artificial neural networks,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Automated brain extraction of multisequence mri using artificial neural networks,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.807266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6aa9da99-5c54-48ab-b4c3-e3ee14344a5d · outbound

This paper cites Panet: Few- shot image semantic segmentation with prototype alignment,.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Panet: Few- shot image semantic segmentation with prototype alignment,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:58:20.792117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47f87918-b84c-40e8-ad6c-5657d456f9d2 · outbound

This paper cites Visualizing data using t-sne.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning Visualizing data using t-sne

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.494744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e36cd261-b613-42b4-92b6-8a76b2878cfa · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-08T19:58:20.499385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers