Paper Citation Record · LEDGER

Recent Advances in Medical Imaging Segmentation: A Survey

As of 19 August 2026, this Paper Citation Record lists 100 of 132 outbound references and 4 inbound Pith citation observations for arXiv:2505.09274.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.09274 v1

Coverage vector

measured 100 of 132 reference resolution

Typed states for the displayed outbound observations.

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measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T04:56:33.261444Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T18:04:58.356486Z

Reference resolution

100 of 132 outbound references displayed

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External citation measurements

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Outbound references

Observation 2d9b5c19-56b0-43a6-ab9e-e345b7715986 · outbound

This paper cites Four challenges in medical image analysis from an industrial perspective,.

Recent Advances in Medical Imaging Segmentation: A Survey Four challenges in medical image analysis from an industrial perspective,

Reference 1

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Observation 62b8fe24-e148-4bd0-8e53-2fbf6726e0df · outbound

This paper cites Anomaly detection-inspired few-shot medical image segmentation through self- supervision with supervoxels,.

Recent Advances in Medical Imaging Segmentation: A Survey Anomaly detection-inspired few-shot medical image segmentation through self- supervision with supervoxels,

Reference 2

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Observation 321c4ee8-b8f9-4b03-bcd0-f0f59a1db908 · outbound

This paper cites Segment anything in medical images,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything in medical images,

Reference 3

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Observation 7958df17-8349-4e2d-8dd5-60089e057be8 · outbound

This paper cites Segment anything model for medical image segmentation: Current applications and future directions,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything model for medical image segmentation: Current applications and future directions,

Reference 4

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Observation 42444562-608f-41dc-af3f-e6886a27d839 · outbound

This paper cites PDAtt- Unet: Pyramid dual-decoder attention unet for covid-19 infection seg- mentation from ct-scans,.

Recent Advances in Medical Imaging Segmentation: A Survey PDAtt- Unet: Pyramid dual-decoder attention unet for covid-19 infection seg- mentation from ct-scans,

Reference 5

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Observation e9f2d2d7-dfe5-4498-a887-6d7ef5f70b87 · outbound

This paper cites U-net and its variants for medical image segmentation: A review of theory and applications,.

Recent Advances in Medical Imaging Segmentation: A Survey U-net and its variants for medical image segmentation: A review of theory and applications,

Reference 6

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Observation 4b0d8d71-b2b1-4df7-8ac1-06f1400bad82 · outbound

This paper cites Medical image segmentation review: The success of U-Net,.

Recent Advances in Medical Imaging Segmentation: A Survey Medical image segmentation review: The success of U-Net,

Reference 7

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Observation 6ea7e69f-70db-4715-80c0-4eb64f451f53 · outbound

This paper cites Advances in medical image analysis with vision transformers: a comprehensive review,.

Recent Advances in Medical Imaging Segmentation: A Survey Advances in medical image analysis with vision transformers: a comprehensive review,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.313005Z

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Observation e0e40448-b24e-4cdf-bd92-9d223c760b22 · outbound

This paper cites Transformers in medical imaging: A survey,.

Recent Advances in Medical Imaging Segmentation: A Survey Transformers in medical imaging: A survey,

Reference 9

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Observation 54063e56-2c76-4bd3-ab0e-2b817fb42703 · outbound

This paper cites Transforming medical imaging with transformers? a comparative re- view of key properties, current progresses, and future perspectives,.

Recent Advances in Medical Imaging Segmentation: A Survey Transforming medical imaging with transformers? a comparative re- view of key properties, current progresses, and future perspectives,

Reference 10

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Observation dad0579a-0d6f-48d1-9ed2-fe762f4b9069 · outbound

This paper cites SynSeg-Net: Synthetic segmentation without target modality ground truth,.

Recent Advances in Medical Imaging Segmentation: A Survey SynSeg-Net: Synthetic segmentation without target modality ground truth,

Reference 11

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Observation 9f301d2b-5b1a-4cc7-88da-1e09bf53a30a · outbound

This paper cites Data augmen- tation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks,.

Recent Advances in Medical Imaging Segmentation: A Survey Data augmen- tation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks,

Reference 12

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Observation f69206fd-edba-47f5-be5c-f206e92e230e · outbound

This paper cites MedSegDi ff-V2: Diffusion-based medical image segmentation with transformer,.

Recent Advances in Medical Imaging Segmentation: A Survey MedSegDi ff-V2: Diffusion-based medical image segmentation with transformer,

Reference 13

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Observation ead227cf-2c49-410b-9ad8-395d36159905 · outbound

This paper cites Prototype correlation matching and class-relation reasoning for few-shot medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Prototype correlation matching and class-relation reasoning for few-shot medical image segmentation,

Reference 14

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Observation 661b64d3-85f3-4dcb-89ea-b16cf68f4f7e · outbound

This paper cites Clip-driven universal model for organ segmentation and tumor detection,.

Recent Advances in Medical Imaging Segmentation: A Survey Clip-driven universal model for organ segmentation and tumor detection,

Reference 15

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Observation 61c1315f-cc10-4dfd-bbd3-077bf4a9ee77 · outbound

This paper cites Tyche: Stochastic in-context learning for medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Tyche: Stochastic in-context learning for medical image segmentation,

Reference 16

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Observation 14150aa9-441f-49e3-866c-1bd8c930c4cf · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Recent Advances in Medical Imaging Segmentation: A Survey Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 17

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Observation efa01282-d199-4708-a47d-3cd0ea38a41e · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Recent Advances in Medical Imaging Segmentation: A Survey Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 18

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Observation 1b42dd8b-a489-411f-ab06-8db6f92fe8b8 · outbound

This paper cites Auto-Encoding Variational Bayes.

Recent Advances in Medical Imaging Segmentation: A Survey Auto-Encoding Variational Bayes

Reference 19

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Observation 1a98918e-c8e6-4459-8eba-40a94c0520d9 · outbound

This paper cites Variational inference with normalizing flows,.

Recent Advances in Medical Imaging Segmentation: A Survey Variational inference with normalizing flows,

Reference 20

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Observation 2b397629-d85a-42d0-8083-c2e6b8d0edd1 · outbound

This paper cites Deep genera- tive modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,.

Recent Advances in Medical Imaging Segmentation: A Survey Deep genera- tive modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

Reference 21

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Observation c8644677-3aec-41a2-9c37-57b8a7c5cb46 · outbound

This paper cites Diffusion mod- els in vision: A survey,.

Recent Advances in Medical Imaging Segmentation: A Survey Diffusion mod- els in vision: A survey,

Reference 22

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Observation 50804f61-a962-4cad-bde8-e224a8c90d31 · outbound

This paper cites an unresolved cited work.

Recent Advances in Medical Imaging Segmentation: A Survey Unresolved cited work

Reference 23

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Observation ee272574-53e9-4f92-af35-dd986fbd240d · outbound

This paper cites Generative adversar- ial nets,.

Recent Advances in Medical Imaging Segmentation: A Survey Generative adversar- ial nets,

Reference 24

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Observation f732c3f4-e911-4467-bf1f-8914090d1a47 · outbound

This paper cites Precomputed real-time texture synthesis with markovian generative adversarial networks,.

Recent Advances in Medical Imaging Segmentation: A Survey Precomputed real-time texture synthesis with markovian generative adversarial networks,

Reference 25

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Observation c8ad0813-4b8d-4775-85c1-a61c9a5afa56 · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks,.

Recent Advances in Medical Imaging Segmentation: A Survey Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 26

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Observation 56b2b787-9456-4270-a957-6203b1ad0d1f · outbound

This paper cites Conditional generative adversarial nets,.

Recent Advances in Medical Imaging Segmentation: A Survey Conditional generative adversarial nets,

Reference 27

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Observation b35f81b0-03a0-4896-b240-4e517746b638 · outbound

This paper cites Conditional image synthesis with auxiliary classifier gans,.

Recent Advances in Medical Imaging Segmentation: A Survey Conditional image synthesis with auxiliary classifier gans,

Reference 28

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Observation d2042bcc-33a3-4267-8d8f-57f8f988017c · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

Recent Advances in Medical Imaging Segmentation: A Survey A style-based generator architecture for generative adversarial networks,

Reference 29

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Observation 5492c50a-e1d5-4458-8826-2944814c4bd6 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Recent Advances in Medical Imaging Segmentation: A Survey Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 30

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Source-reported events for the cited work

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Observation 31c3db4c-7f0f-4a1c-b12e-4a2e006e1e0c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Recent Advances in Medical Imaging Segmentation: A Survey Score-Based Generative Modeling through Stochastic Differential Equations

Reference 31

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Observation ac4e706d-b391-4ddc-a9d3-234953b16b58 · outbound

This paper cites Denoising di ffusion probabilistic mod- els,.

Recent Advances in Medical Imaging Segmentation: A Survey Denoising di ffusion probabilistic mod- els,

Reference 32

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Observation a69ffabf-564a-4988-b54f-b649469faf95 · outbound

This paper cites Di ffusion models beat gans on image syn- thesis,.

Recent Advances in Medical Imaging Segmentation: A Survey Di ffusion models beat gans on image syn- thesis,

Reference 33

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Observation bd27632f-f894-4766-a345-0b965d2c3ca0 · outbound

This paper cites Classifier-free di ffusion guidance,.

Recent Advances in Medical Imaging Segmentation: A Survey Classifier-free di ffusion guidance,

Reference 34

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Observation 2da545d8-f27b-4aab-9e90-74646afdb398 · outbound

This paper cites Deep adversarial training for multi-organ nuclei segmentation in histopathology images,.

Recent Advances in Medical Imaging Segmentation: A Survey Deep adversarial training for multi-organ nuclei segmentation in histopathology images,

Reference 35

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Observation 0d1c32bb-2435-4e27-a400-af3f3d2ef372 · outbound

This paper cites Translating and segmenting mul- timodal medical volumes with cycle-and shape-consistency generative adversarial network,.

Recent Advances in Medical Imaging Segmentation: A Survey Translating and segmenting mul- timodal medical volumes with cycle-and shape-consistency generative adversarial network,

Reference 36

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Observation a88097a2-083c-4b28-bae0-13c799ba83bc · outbound

This paper cites MedSegDiff: Medical image segmenta- tion with diffusion probabilistic model,.

Recent Advances in Medical Imaging Segmentation: A Survey MedSegDiff: Medical image segmenta- tion with diffusion probabilistic model,

Reference 37

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This paper cites Deep adversarial networks for biomedical image segmentation utilizing unannotated images,.

Recent Advances in Medical Imaging Segmentation: A Survey Deep adversarial networks for biomedical image segmentation utilizing unannotated images,

Reference 38

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Observation d4cb03fc-eabc-48f9-ae0a-13877f1eaf8e · outbound

This paper cites Self-supervised vessel segmentation via adversarial learning,.

Recent Advances in Medical Imaging Segmentation: A Survey Self-supervised vessel segmentation via adversarial learning,

Reference 39

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Observation 71234ac4-807e-4e65-abe1-f21bc34cdc16 · outbound

This paper cites Diffusion adversarial representation learn- ing for self-supervised vessel segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Diffusion adversarial representation learn- ing for self-supervised vessel segmentation,

Reference 40

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Observation af10db1b-c270-4903-a727-5306cba37d90 · outbound

This paper cites C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation,

Reference 41

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Observation 3afb3b85-fced-4cc7-832e-24087378f92b · outbound

This paper cites Spine-GAN: Seman- tic segmentation of multiple spinal structures,.

Recent Advances in Medical Imaging Segmentation: A Survey Spine-GAN: Seman- tic segmentation of multiple spinal structures,

Reference 42

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Observation dde25762-0228-41aa-8147-bd7da533c3be · outbound

This paper cites Automatic segmentation of coronary arteries in x-ray angiograms us- ing multiscale analysis and artificial neural networks,.

Recent Advances in Medical Imaging Segmentation: A Survey Automatic segmentation of coronary arteries in x-ray angiograms us- ing multiscale analysis and artificial neural networks,

Reference 43

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This paper cites Sequential vessel segmentation via deep channel attention network,.

Recent Advances in Medical Imaging Segmentation: A Survey Sequential vessel segmentation via deep channel attention network,

Reference 44

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Observation 692c6e8e-b470-4959-ba76-63fc12864879 · outbound

This paper cites Locating blood ves- sels in retinal images by piecewise threshold probing of a matched filter response,.

Recent Advances in Medical Imaging Segmentation: A Survey Locating blood ves- sels in retinal images by piecewise threshold probing of a matched filter response,

Reference 45

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This paper cites REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening.

Recent Advances in Medical Imaging Segmentation: A Survey REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

Reference 46

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This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Recent Advances in Medical Imaging Segmentation: A Survey The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 47

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This paper cites An open access thyroid ultra- sound image database,.

Recent Advances in Medical Imaging Segmentation: A Survey An open access thyroid ultra- sound image database,

Reference 48

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Observation c2ce0c1e-f49d-4e64-b777-ba682ae42e78 · outbound

This paper cites AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,

Reference 49

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This paper cites Automatic multi-organ segmentation on abdominal CT with dense v-networks,.

Recent Advances in Medical Imaging Segmentation: A Survey Automatic multi-organ segmentation on abdominal CT with dense v-networks,

Reference 50

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Observation bb68cfbe-adc8-476f-bb41-14747ac8982d · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Recent Advances in Medical Imaging Segmentation: A Survey Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 51

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This paper cites Semantic image syn- thesis with spatially-adaptive normalization,.

Recent Advances in Medical Imaging Segmentation: A Survey Semantic image syn- thesis with spatially-adaptive normalization,

Reference 52

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This paper cites One-shot learn- ing for semantic segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey One-shot learn- ing for semantic segmentation,

Reference 53

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This paper cites PANet: Few-shot image seman- tic segmentation with prototype alignment,.

Recent Advances in Medical Imaging Segmentation: A Survey PANet: Few-shot image seman- tic segmentation with prototype alignment,

Reference 54

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This paper cites Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks,.

Recent Advances in Medical Imaging Segmentation: A Survey Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks,

Reference 55

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This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.

Recent Advances in Medical Imaging Segmentation: A Survey Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 56

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Observation 9364b276-1d07-4252-b05c-82c704c652bc · outbound

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

Recent Advances in Medical Imaging Segmentation: A Survey CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation,

Reference 57

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Observation f16055ef-a569-4e38-80f4-40a4ce026b13 · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source images,.

Recent Advances in Medical Imaging Segmentation: A Survey Multivariate mixture model for myocardial segmentation combining multi-source images,

Reference 58

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Observation 0fa457f1-9d20-41e9-aaa0-c3bf958936bb · outbound

This paper cites Spatial context-aware self-attention model for multi-organ segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Spatial context-aware self-attention model for multi-organ segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.921769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2129123-b3dd-4950-8ecf-ffb2289d8913 · outbound

This paper cites Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration,.

Recent Advances in Medical Imaging Segmentation: A Survey Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.908822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82253a30-6a36-4be6-b372-dcb8e94dc9b3 · outbound

This paper cites ‘squeeze & excite’guided few-shot segmentation of volumetric images,.

Recent Advances in Medical Imaging Segmentation: A Survey ‘squeeze & excite’guided few-shot segmentation of volumetric images,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.895534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 215552b3-3b41-4f9e-82aa-4f1c40c43db2 · outbound

This paper cites Self-supervision with superpixels: 20 Fares BOUGOURZI et al. / Medical Image Analysis (2025) Training few-shot medical image segmentation without annotation,.

Recent Advances in Medical Imaging Segmentation: A Survey Self-supervision with superpixels: 20 Fares BOUGOURZI et al. / Medical Image Analysis (2025) Training few-shot medical image segmentation without annotation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.882321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f813d299-0997-40bc-b47b-ab414abfd5e5 · outbound

This paper cites Recurrent mask refinement for few-shot medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Recurrent mask refinement for few-shot medical image segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.869602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03b52831-8efd-4e64-abde-d7d43d003084 · outbound

This paper cites Few shot medical im- age segmentation with cross attention transformer,.

Recent Advances in Medical Imaging Segmentation: A Survey Few shot medical im- age segmentation with cross attention transformer,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.857006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc2f9911-4097-494b-8838-27b2fa3fbb2c · outbound

This paper cites Rethinking few-shot medical segmenta- tion: a vector quantization view,.

Recent Advances in Medical Imaging Segmentation: A Survey Rethinking few-shot medical segmenta- tion: a vector quantization view,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.843952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 874cbd9c-4295-4a69-8a55-b64cecfb699d · outbound

This paper cites Dual contrastive learning with anatom- ical auxiliary supervision for few-shot medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Dual contrastive learning with anatom- ical auxiliary supervision for few-shot medical image segmentation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.830689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c0fc6bb3-2ad8-45bd-b719-af48da941acc · outbound

This paper cites Learning what and where to segment: A new perspective on medical image few-shot segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey Learning what and where to segment: A new perspective on medical image few-shot segmentation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.817580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 91872512-9bc4-4249-9d5d-87bf67f7da0d · outbound

This paper cites Language models are few-shot learners,.

Recent Advances in Medical Imaging Segmentation: A Survey Language models are few-shot learners,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.804798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 23680fe0-d265-413b-941a-80ea8e471e73 · outbound

This paper cites GPT-4 Technical Report.

Recent Advances in Medical Imaging Segmentation: A Survey GPT-4 Technical Report

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.564488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 766a31e9-6450-4c80-b4f0-1bea908bc242 · outbound

This paper cites PaLM: Scaling language modeling with pathways,.

Recent Advances in Medical Imaging Segmentation: A Survey PaLM: Scaling language modeling with pathways,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.792029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49e81892-a5cf-40ee-ac00-ec28538624b3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Recent Advances in Medical Imaging Segmentation: A Survey LLaMA: Open and Efficient Foundation Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.572676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c962e812-246c-4520-9fb7-bcec363e3ad1 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Recent Advances in Medical Imaging Segmentation: A Survey Learning transferable visual models from natural language supervision,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.779012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12466527-5741-45ff-82cc-0179aaf7cb62 · outbound

This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

Recent Advances in Medical Imaging Segmentation: A Survey BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.766186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 707c242f-96f9-4e6a-a519-4bcbf2c45068 · outbound

This paper cites Segment anything,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.753174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2606ebe4-8e53-4494-b0ef-072d68e34634 · outbound

This paper cites Segment everything everywhere all at once,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment everything everywhere all at once,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.740260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8896ac0-dbb6-4c61-9f4c-ced3965b9177 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Recent Advances in Medical Imaging Segmentation: A Survey An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.592474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5a1fa9-a92f-45e6-a905-26d909e929fc · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Recent Advances in Medical Imaging Segmentation: A Survey Masked autoencoders are scalable vision learners,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.727066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63c88f15-4e36-45bc-b431-074dbc5dac6b · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains,.

Recent Advances in Medical Imaging Segmentation: A Survey Fourier features let networks learn high frequency functions in low dimensional domains,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.714415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 960fdd93-cd35-42c8-b162-30b386c1b314 · outbound

This paper cites Sam.md: Zero-shot medical image segmentation capabilities of the segment anything model,.

Recent Advances in Medical Imaging Segmentation: A Survey Sam.md: Zero-shot medical image segmentation capabilities of the segment anything model,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.701861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1a1e2b0-0ddf-49d5-8df7-8349d67ce5b8 · outbound

This paper cites Segment anything model for medical image analysis: an experimental study,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything model for medical image analysis: an experimental study,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.688975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c674d53-bdc4-49d6-8bb0-d203ed19fe67 · outbound

This paper cites Segment anything model for medical images?.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything model for medical images?

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.676596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8a26e144-c2eb-4117-88d0-27b40ffa8ed8 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Recent Advances in Medical Imaging Segmentation: A Survey Customized Segment Anything Model for Medical Image Segmentation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.616281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5154389-e070-4b71-b78c-8fb9adc7716c · outbound

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

Recent Advances in Medical Imaging Segmentation: A Survey TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.620562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee5a913-1da7-454b-ba61-4fb167bbd599 · outbound

This paper cites SAM-Med2D.

Recent Advances in Medical Imaging Segmentation: A Survey SAM-Med2D

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.624729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e7f4315-b08b-4541-b181-14f349f3eabe · outbound

This paper cites S-SAM: SVD- Based Fine-Tuning of segment anything model for medical image seg- mentation,.

Recent Advances in Medical Imaging Segmentation: A Survey S-SAM: SVD- Based Fine-Tuning of segment anything model for medical image seg- mentation,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.663743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7cc186b7-92e7-4b73-a037-b6103544e969 · outbound

This paper cites AdaptiveSAM: Towards ef- ficient tuning of SAM for surgical scene segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey AdaptiveSAM: Towards ef- ficient tuning of SAM for surgical scene segmentation,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.650893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99da778b-ffaf-4a32-a8f3-eea32396dda6 · outbound

This paper cites SAM-Path: A segment anything model for semantic segmentation in digital pathology,.

Recent Advances in Medical Imaging Segmentation: A Survey SAM-Path: A segment anything model for semantic segmentation in digital pathology,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.638227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c10bde58-3034-4c2f-ae89-64e282c9217c · outbound

This paper cites Structured crowdsourcing en- ables convolutional segmentation of histology images,.

Recent Advances in Medical Imaging Segmentation: A Survey Structured crowdsourcing en- ables convolutional segmentation of histology images,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.625459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d51eebb-505e-4b5c-a705-b77ec9b6db9b · outbound

This paper cites MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images,.

Recent Advances in Medical Imaging Segmentation: A Survey MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.612753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40e828cb-8195-4a63-9261-c7d4dbafcc62 · outbound

This paper cites Input augmentation with sam: Boost- ing medical image segmentation with segmentation foundation model,.

Recent Advances in Medical Imaging Segmentation: A Survey Input augmentation with sam: Boost- ing medical image segmentation with segmentation foundation model,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.599597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3481c6e-0eb2-4ff5-b949-ec4a2399ca93 · outbound

This paper cites 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation.

Recent Advances in Medical Imaging Segmentation: A Survey 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.654153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34f4bbf-2600-45c9-aac8-4d7295425360 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Recent Advances in Medical Imaging Segmentation: A Survey Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:10.658364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a4cff71-c5f9-4b28-9c9d-0122fe0896b4 · outbound

This paper cites SAM-Med3D: Towards general-purpose segmentation models for volumetric medical images,.

Recent Advances in Medical Imaging Segmentation: A Survey SAM-Med3D: Towards general-purpose segmentation models for volumetric medical images,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.586549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc4607c4-305b-4c6c-bb50-9ccf2b653123 · outbound

This paper cites MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.573968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4b4866cc-9be1-4846-a672-7f9a38bb07ba · outbound

This paper cites FastSAM3D: An e fficient segment any- thing model for 3D volumetric medical images,.

Recent Advances in Medical Imaging Segmentation: A Survey FastSAM3D: An e fficient segment any- thing model for 3D volumetric medical images,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.560561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 849db7e2-aa5d-4136-a628-838cf3deef05 · outbound

This paper cites Segment anything model for semi- supervised medical image segmentation via selecting reliable pseudo- labels,.

Recent Advances in Medical Imaging Segmentation: A Survey Segment anything model for semi- supervised medical image segmentation via selecting reliable pseudo- labels,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.547478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c042b28e-5a5a-49d2-afba-a96fc0c9fb06 · outbound

This paper cites Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?.

Recent Advances in Medical Imaging Segmentation: A Survey Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.535087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f70f8f64-e418-4727-a0ce-d07d20f36336 · outbound

This paper cites SemiSAM: Exploring sam for enhanc- ing semi-supervised medical image segmentation with extremely limited annotations,.

Recent Advances in Medical Imaging Segmentation: A Survey SemiSAM: Exploring sam for enhanc- ing semi-supervised medical image segmentation with extremely limited annotations,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.522089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27e9afee-7200-42e1-ad23-1fda656bb51c · outbound

This paper cites A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac mag- netic resonance imaging,.

Recent Advances in Medical Imaging Segmentation: A Survey A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac mag- netic resonance imaging,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.509068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87c2c7d2-f221-40c1-9167-6a8b1488570c · outbound

This paper cites SurgicalSAM: Efficient class prompt- able surgical instrument segmentation,.

Recent Advances in Medical Imaging Segmentation: A Survey SurgicalSAM: Efficient class prompt- able surgical instrument segmentation,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:38:11.496375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation f24967f9-1fd8-4362-919d-586f9eecf4f1 · inbound

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide cites this paper.

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide Recent Advances in Medical Imaging Segmentation: A Survey

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:10.691684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b93bbfa2-5bf5-420c-89ce-7e85be1752c5 · inbound

Lighting-aware Unified Model for Instance Segmentation cites this paper.

Lighting-aware Unified Model for Instance Segmentation Recent Advances in Medical Imaging Segmentation: A Survey

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:04:45.449551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cda998ae-764c-4e08-b9c5-2fa504e6a960 · inbound

Lighting-aware Unified Model for Instance Segmentation cites this paper.

Lighting-aware Unified Model for Instance Segmentation Recent Advances in Medical Imaging Segmentation: A Survey

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:04:58.358114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f326883-9392-4acb-a4bd-3582731669cd · inbound

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function cites this paper.

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function Recent Advances in Medical Imaging Segmentation: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-15T04:56:33.261444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.