Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network | Paper Citation Record

Observation 353b0d3f-6eef-4ff5-93fe-54851672c7a7 · outbound

This paper cites Deep belief network modeling for automatic liver seg- mentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Deep belief network modeling for automatic liver seg- mentation

Reference 1

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Observation 298f998b-4707-4107-9802-7c793d2ca5b7 · outbound

This paper cites V oxelMorph: A learning frame- work for deformable medical image registration.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network V oxelMorph: A learning frame- work for deformable medical image registration

Reference 2

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Observation e05c747b-a23e-4529-971b-870ed7518ef2 · outbound

This paper cites V ol2Flow: Segment 3D volumes using a sequence of registration flows.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network V ol2Flow: Segment 3D volumes using a sequence of registration flows

Reference 3

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This paper cites 3D image segmentation with sparse annotation by self-training and internal registration.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network 3D image segmentation with sparse annotation by self-training and internal registration

Reference 4

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Observation a8bd9554-3f7a-4241-91aa-0ecc322918e9 · outbound

This paper cites Swin-UNet: UNet-like pure Transformer for medical image segmenta- tion.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Swin-UNet: UNet-like pure Transformer for medical image segmenta- tion

Reference 5

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Observation 862c1ea6-1b18-4aaf-898b-fed08e10ccb0 · outbound

This paper cites A methodological approach to the classifica- tion of dermoscopy images.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network A methodological approach to the classifica- tion of dermoscopy images

Reference 6

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Observation e0ea3dc7-4189-4d5f-a09c-84f73a0317f8 · outbound

This paper cites Weakly- supervised semantic segmentation via sub-category explo- ration.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Weakly- supervised semantic segmentation via sub-category explo- ration

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.715048Z

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Observation c670136a-ebae-48f3-a4f4-be6289db30f1 · outbound

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

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 8

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Observation 65bc44eb-12d6-4241-be4a-bd732d392337 · outbound

This paper cites Angelini, Yike Guo, and Wenjia Bai.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Angelini, Yike Guo, and Wenjia Bai

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.687035Z

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Observation c2c8c307-c6bc-4e86-b37b-86945767b1bf · outbound

This paper cites Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Reference 10

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Observation e3bbdde5-67ee-41cc-a14a-dbc5f69d92bb · outbound

This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 11

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Observation 403978c2-59c2-4191-8117-a63b48787d84 · outbound

This paper cites ConvFormer: Combining CNN and Transformer for medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network ConvFormer: Combining CNN and Transformer for medical image segmentation

Reference 12

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Observation bec50499-08fa-49e3-9001-8ce174fa2dbc · outbound

This paper cites kCBAC-Net: Deeply supervised complete bipartite networks with asymmetric convolutions for medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network kCBAC-Net: Deeply supervised complete bipartite networks with asymmetric convolutions for medical image segmentation

Reference 13

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Observation d5e07351-95b7-48e1-bb51-7678fc46f48e · outbound

This paper cites UNETR: Transformers for 3D medi- cal image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network UNETR: Transformers for 3D medi- cal image segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.538732Z

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Observation a46fbb86-761b-4dae-a87a-9f0d65348729 · outbound

This paper cites Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 15

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Observation 5d31d702-dc99-4210-998d-ec48d380fc36 · outbound

This paper cites C4kc kits challenge kidney tumor segmentation dataset,.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network C4kc kits challenge kidney tumor segmentation dataset,

Reference 16

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Observation 04376840-d74f-4cb4-afc1-abfc4a82b92d · outbound

This paper cites When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Reference 17

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Observation 335a2852-d4d3-44ec-8e9d-a777ed5dcef0 · outbound

This paper cites Weakly-supervised semantic segmentation network with deep seeded region growing.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Weakly-supervised semantic segmentation network with deep seeded region growing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.449103Z

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Observation 09ee6cda-7bb0-4834-8a17-57be1a920801 · outbound

This paper cites Raman, Demetri Ter- zopoulos, and Kyunghyun Sung.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Raman, Demetri Ter- zopoulos, and Kyunghyun Sung

Reference 19

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Observation a76ac518-df61-4199-b70f-3309b1eb324d · outbound

This paper cites nnU-Net: A self- configuring method for deep learning-based biomedical im- age segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network nnU-Net: A self- configuring method for deep learning-based biomedical im- age segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.370980Z

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Observation f312acef-a662-4ff4-8c0f-6dbabe583338 · outbound

This paper cites Convolution-free medical image segmentation using Transformers.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Convolution-free medical image segmentation using Transformers

Reference 21

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Observation afe2808c-26dc-49a3-b4f7-a1dc24388c75 · outbound

This paper cites CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation

Reference 22

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This paper cites Emre Kavur, N.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Emre Kavur, N

Reference 23

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Observation ded11a05-1a38-42ad-a894-a871a4a59dff · outbound

This paper cites Simple does it: Weakly supervised instance and semantic segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Simple does it: Weakly supervised instance and semantic segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.234534Z

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Observation 4fc6fe27-84b9-4558-a2bd-4fe9ac0ad5f8 · outbound

This paper cites Segment Anything.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Segment Anything

Reference 25

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Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Unresolved cited work

Reference 26

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This paper cites A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

Reference 27

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Observation f9c854b0-fd98-4f13-9a17-d3729e229d5c · outbound

This paper cites PLN: Parasitic-like network for barely supervised medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network PLN: Parasitic-like network for barely supervised medical image segmentation

Reference 28

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Observation 6ef50b91-1917-4f9f-8c46-5edf0821fda4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Decoupled Weight Decay Regularization

Reference 29

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This paper cites Segment Anything in Medical Images.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Segment Anything in Medical Images

Reference 30

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Observation 3c045a90-5a0b-458e-9f8b-b5e6876b4ca0 · outbound

This paper cites Segment Anything Model for Medical Image Analysis: an Experimental Study.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Segment Anything Model for Medical Image Analysis: an Experimental Study

Reference 31

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This paper cites SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning

Reference 32

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Observation d18a57e7-d9f4-4712-ba87-9babe5256eef · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Attention U-Net: Learning Where to Look for the Pancreas

Reference 33

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Observation 337b04d5-2c9d-457b-8ad3-00027f1b1e1e · outbound

This paper cites Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

Reference 34

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Observation 1abc90c4-b8cc-40a5-b780-347ec5a352f0 · outbound

This paper cites Interactive whole-heart segmentation in congenital heart disease.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Interactive whole-heart segmentation in congenital heart disease

Reference 35

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Observation af71b346-d925-41d2-9a3a-a700f6829df0 · outbound

This paper cites Murphy, and Alan L.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Murphy, and Alan L

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.078024Z

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This paper cites Riedlinger, Subhajyoti De, Shaoting Zhang, and Dimitris N.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Riedlinger, Subhajyoti De, Shaoting Zhang, and Dimitris N

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.039862Z

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This paper cites Girshick, Georgia Gkioxari, and Kaiming He.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Girshick, Georgia Gkioxari, and Kaiming He

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:35.008259Z

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This paper cites U- Net: Convolutional networks for biomedical image segmen- tation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network U- Net: Convolutional networks for biomedical image segmen- tation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.985049Z

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This paper cites Turkbey, Le Lu, Ji- amin Liu, and Ronald M.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Turkbey, Le Lu, Ji- amin Liu, and Ronald M

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.958621Z

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Observation e41bb701-de04-4a4d-8ecf-5945b164b330 · outbound

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Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Unresolved cited work

Reference 41

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Observation 590f1276-bc4c-4234-ae9c-719a65f778d2 · outbound

This paper cites SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

Reference 42

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Observation 3fc3c48e-27b6-456a-a0f4-50c26dbbfe83 · outbound

This paper cites UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation

Reference 43

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Observation dd5e4fbf-9a31-40a8-9cfa-4e83d780e690 · outbound

This paper cites An active learning approach for reduc- ing annotation cost in skin lesion analysis.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network An active learning approach for reduc- ing annotation cost in skin lesion analysis

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.890005Z

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This paper cites A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Reference 45

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This paper cites 3D image reconstruction for comparison of algorithm database: A patient specific anatomical and medical image database.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network 3D image reconstruction for comparison of algorithm database: A patient specific anatomical and medical image database

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.861272Z

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Observation f286a3fe-8b6d-4d55-8948-d62f6392a094 · outbound

This paper cites Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.834047Z

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Observation 7e556a0f-952e-4f46-967e-006cb23ef3e7 · outbound

This paper cites Revisiting Rubik’s cube: Self-supervised learning with volume-wise transformation for 3D medical image seg- mentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Revisiting Rubik’s cube: Self-supervised learning with volume-wise transformation for 3D medical image seg- mentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.771326Z

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Observation 40117b23-aa99-4263-8e2a-10be4f876c15 · outbound

This paper cites A multiple layer U-Net, U n-Net, for liver and liver tumor seg- mentation in CT.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network A multiple layer U-Net, U n-Net, for liver and liver tumor seg- mentation in CT

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.739154Z

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Observation 0fd9afe4-8a5e-457a-b1f0-a7e336756451 · outbound

This paper cites 3D segmentation in the clinic: A grand challenge.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network 3D segmentation in the clinic: A grand challenge

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.708189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55588f7b-f657-4c4f-a301-195592053991 · outbound

This paper cites Zuluaga, Rosalind Pratt, Premal A.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Zuluaga, Rosalind Pratt, Premal A

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.679049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0d201121-8fc5-47b9-94f0-51c0b67d13ee · outbound

This paper cites Annotation-efficient deep learn- ing for automatic medical image segmentation.Nature Com- munications, 12(1):1–13, 2021.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Annotation-efficient deep learn- ing for automatic medical image segmentation.Nature Com- munications, 12(1):1–13, 2021

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.646130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4e839a60-5900-4b88-8057-bdac0cc1ab53 · outbound

This paper cites ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T15:51:33.193290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bc65d79-1401-427c-9385-677d8023075d · outbound

This paper cites Self-learning and one-shot learning based single- slice annotation for 3D medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Self-learning and one-shot learning based single- slice annotation for 3D medical image segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.609192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85c938b2-b09b-4657-a7ab-1e888b2fc3c0 · outbound

This paper cites CoTr: Efficiently bridging CNN and Transformer for 3D medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network CoTr: Efficiently bridging CNN and Transformer for 3D medical image segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.569874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 276d8508-f59d-4ffb-bf1d-2fe1a3e3eaa8 · outbound

This paper cites CAMEL: A weakly supervised learning framework for histopathology image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network CAMEL: A weakly supervised learning framework for histopathology image segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.520365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 75e9c860-bb90-4d79-99f5-8ceb3025f9c4 · outbound

This paper cites Weakly supervised histopathology cancer im- age segmentation and classification.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Weakly supervised histopathology cancer im- age segmentation and classification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.490304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f5815cb7-55f1-40bc-9a38-cf3a38f6fc20 · outbound

This paper cites an unresolved cited work.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:51:34.459691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1c988faa-642f-414d-8d35-33ae0bcc7036 · outbound

This paper cites Sli2V ol: Annotate a 3D volume from a single slice with self- supervised learning.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Sli2V ol: Annotate a 3D volume from a single slice with self- supervised learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.432668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 016e8dd5-23cd-4099-9341-43f16ad0eb2e · outbound

This paper cites Interactive medical image segmentation via a point-based interaction.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Interactive medical image segmentation via a point-based interaction

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.403614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4886e601-8044-49d1-b60a-9aa35380f494 · outbound

This paper cites A point in the right di- rection: Vector prediction for spatially-aware self-supervised volumetric representation learning.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network A point in the right di- rection: Vector prediction for spatially-aware self-supervised volumetric representation learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.377215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79c62707-96d3-46c8-b35f-14215b4634cc · outbound

This paper cites Keep your friends close & enemies farther: Debiasing contrastive learning with spatial priors in 3D radiology images.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Keep your friends close & enemies farther: Debiasing contrastive learning with spatial priors in 3D radiology images

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.346772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ffaa8328-1fa5-4db4-aeca-f9eeb31342ed · outbound

This paper cites Hughes, and Danny Z.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Hughes, and Danny Z

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.318885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d5e59358-d355-482e-bba3-6632dd93d746 · outbound

This paper cites an unresolved cited work.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:51:34.283775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 004aefc8-e821-4abd-9ef0-e4821a1ed2b0 · outbound

This paper cites an unresolved cited work.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:51:34.253130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 06b9cdf1-cb0d-4097-bfcd-43d9b3d7edfc · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T15:51:33.317610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e6c453-e43b-4049-ac52-e312b5936bc6 · outbound

This paper cites Can SAM Segment Polyps?.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Can SAM Segment Polyps?

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T15:51:33.327836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76e73a74-c1e6-43d8-95c9-f574f3947d75 · outbound

This paper cites Fishman, and Alan L.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Fishman, and Alan L

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.215049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 386412e6-78a5-48c9-88b5-dbc7aa3fc0b7 · outbound

This paper cites UNet++: A nested U-Net architecture for medical image segmentation.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network UNet++: A nested U-Net architecture for medical image segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.186701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation efc70300-4182-4bb1-9a14-0305f3c84d80 · outbound

This paper cites Gotway, and Jianming Liang.

Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network Gotway, and Jianming Liang

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:51:34.148584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers