Paper Citation Record · LEDGER

In-context learning for medical image segmentation

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2412.13299.

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

pith.paper-citation-record.v1
2412.13299 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:18:09.824010Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:47.322998Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T17:34:47.438802Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy34
unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7541d61-7941-4a2a-bfc5-7c74781f30b4 · outbound

This paper cites A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities.

In-context learning for medical image segmentation A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.456723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01ee7b71-f4d5-4d99-8f68-fa766231b1a8 · outbound

This paper cites Medical Image Segmentation Review: The Success of U-Net.

In-context learning for medical image segmentation Medical Image Segmentation Review: The Success of U-Net

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.441313Z

Source-reported events for the cited work

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Observation 2d8eea7d-9ed5-433d-877d-b05c6cbe37eb · outbound

This paper cites Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework.

In-context learning for medical image segmentation Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.425475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e831cf3-62a1-4208-b0cb-25ba3f158ed9 · outbound

This paper cites Harrison, H.

In-context learning for medical image segmentation Harrison, H

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.409545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 54f64a25-aecd-4a7f-83a8-45e54c37c2da · outbound

This paper cites DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation.

In-context learning for medical image segmentation DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.394889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 12a705b3-c25f-4e70-b943-4b721e5149be · outbound

This paper cites Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

In-context learning for medical image segmentation Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.380410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c6a9954-3b5a-4495-83f6-c00c986df47f · outbound

This paper cites Radiomics model of diffusion- weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer.

In-context learning for medical image segmentation Radiomics model of diffusion- weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.367183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation daf473cd-4b5c-44e8-b174-3730b90d40c3 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

In-context learning for medical image segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.354080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1f4d5e1-924b-413e-ab1c-416ccde2380d · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

In-context learning for medical image segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.341437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b4a77f9b-2d87-48bd-91d0-f254b2844ad7 · outbound

This paper cites Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation.

In-context learning for medical image segmentation Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.327503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dd9012f-11c3-49f4-8f9a-3ddd67e6985f · outbound

This paper cites Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine E.

In-context learning for medical image segmentation Robson, Brett Marinelli, Mingqian Huang, Amish Doshi, Adam Jacobi, Chendi Cao, Katherine E

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.313180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60ead64d-ce6d-494a-b054-177b48396311 · outbound

This paper cites Fayad, Timothy Deyer, and Xueyan Mei.

In-context learning for medical image segmentation Fayad, Timothy Deyer, and Xueyan Mei

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.299323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd5cb0b9-aa6a-48ee-8f02-8e7a568eb65e · outbound

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

In-context learning for medical image segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 13

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Source-reported events for the cited work

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Observation 29ff5e8d-4031-4b00-b07b-5a0b30a8c8bb · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

In-context learning for medical image segmentation Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.684763Z

Source-reported events for the cited work

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Observation 12d78ae9-e5a5-4dc9-9f11-0022d1e1dcfb · outbound

This paper cites Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation.

In-context learning for medical image segmentation Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.283782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4a3ae260-aa2c-4d75-ad12-e04a920fedd5 · outbound

This paper cites Domain Adaptation for Medical Image Analysis: A Survey.

In-context learning for medical image segmentation Domain Adaptation for Medical Image Analysis: A Survey

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.270229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57cb27d0-a7d8-4a39-8b0d-774d3e593cc9 · outbound

This paper cites Wood, Holger Roth, Andriy Myronenko, Daguang Xu, and Ziyue Xu.

In-context learning for medical image segmentation Wood, Holger Roth, Andriy Myronenko, Daguang Xu, and Ziyue Xu

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.256396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ba570df-9e13-4832-86bd-115eeb5eed78 · outbound

This paper cites Morris, Moozhan Nikpanah, Arman Rahmim, Yanji Xu, Anne Pariser, Michael T.

In-context learning for medical image segmentation Morris, Moozhan Nikpanah, Arman Rahmim, Yanji Xu, Anne Pariser, Michael T

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.242620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 46a033aa-a681-45bf-89f7-aa7772f553d2 · outbound

This paper cites Sabuncu, John Guttag, and Adrian V.

In-context learning for medical image segmentation Sabuncu, John Guttag, and Adrian V

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.227441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3fc83edd-975f-4cdb-80ba-62867ada9449 · outbound

This paper cites Pace, Hannah T.

In-context learning for medical image segmentation Pace, Hannah T

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.209906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bdfd39aa-d9ad-46db-b8d4-1ce5ba813196 · outbound

This paper cites Current methods in medical image segmentation.

In-context learning for medical image segmentation Current methods in medical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.193928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69b398f0-1ab0-4f09-945d-24da067d7388 · outbound

This paper cites Medical image segmentation using k-means clustering and improved watershed algorithm.

In-context learning for medical image segmentation Medical image segmentation using k-means clustering and improved watershed algorithm

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.179333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f35226fc-9258-4364-8f19-2535603647fc · outbound

This paper cites an unresolved cited work.

In-context learning for medical image segmentation Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:18:10.165113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d6ff1ca7-c640-42cf-a3c4-6369057ee328 · outbound

This paper cites Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification.

In-context learning for medical image segmentation Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.151928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c189c7fb-acac-424b-8e6a-443fbd4d3c04 · outbound

This paper cites Analyzing Training Information From Random Forests for Improved Image Segmentation.

In-context learning for medical image segmentation Analyzing Training Information From Random Forests for Improved Image Segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.137944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a00263c-c534-44db-832e-8d9d43b578f9 · outbound

This paper cites Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images.

In-context learning for medical image segmentation Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.122338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6cda0af2-a9fb-41f6-837b-3dcac094e887 · outbound

This paper cites Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, and Yuyin Zhou.

In-context learning for medical image segmentation Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, and Yuyin Zhou

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.106516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 707e1e1a-e3fc-4e7f-90a9-4c193bd06c18 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

In-context learning for medical image segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.748851Z

Source-reported events for the cited work

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Observation 56529c30-8820-4f4a-a56c-70916ff927d9 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick.

In-context learning for medical image segmentation Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.082861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8fb01632-41fd-4004-a79e-40339e55b135 · outbound

This paper cites A Survey on In-context Learning.

In-context learning for medical image segmentation A Survey on In-context Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.757916Z

Source-reported events for the cited work

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Observation 416fdcfb-7c2e-43b1-8b46-7cf4c71b945a · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

In-context learning for medical image segmentation Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.762835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b27cb75-1621-4839-abdb-f5ce81fa2a02 · outbound

This paper cites Language models are few-shot learners.

In-context learning for medical image segmentation Language models are few-shot learners

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.069199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d5df9e5-15db-415a-b534-f6ba0ec63fa0 · outbound

This paper cites Transformers learn in-context by gradient descent.

In-context learning for medical image segmentation Transformers learn in-context by gradient descent

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.773508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3060f70c-0b7b-4a7e-984e-8ba2009c3805 · outbound

This paper cites What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems, 36:17773–17794, 2023.

In-context learning for medical image segmentation What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems, 36:17773–17794, 2023

Reference 34

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Source-reported events for the cited work

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Observation 28c38fd5-a64a-4a02-ae51-049e8634e7de · outbound

This paper cites van Engelen and Holger H.

In-context learning for medical image segmentation van Engelen and Holger H

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.047592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db23550d-22ea-4b00-9977-7e4f64a28070 · outbound

This paper cites Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation.

In-context learning for medical image segmentation Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.034286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6fbc8416-fdc4-4a9d-9085-3381e3018f89 · outbound

This paper cites Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.

In-context learning for medical image segmentation Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:10.017431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3c71ac13-6350-4225-bf0c-8d404b37d912 · outbound

This paper cites Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation.

In-context learning for medical image segmentation Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:09.995301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 969c0b1d-d655-44a0-8279-abaa58e33fb6 · outbound

This paper cites GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation.

In-context learning for medical image segmentation GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T13:18:09.803305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b9d1401-453c-4c9b-a2cf-1c38775a978c · outbound

This paper cites Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels.

In-context learning for medical image segmentation Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:09.978844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0eb5b6f0-743b-494a-8c6a-d376ca8fd1c7 · outbound

This paper cites Maybank, and Dacheng Tao.

In-context learning for medical image segmentation Maybank, and Dacheng Tao

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:09.965200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 48f09c98-388d-4e45-8af1-936810423bf9 · outbound

This paper cites Cold-start active learning for image classification.

In-context learning for medical image segmentation Cold-start active learning for image classification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:09.952188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ba328e8-d1b5-472e-8745-e90d2f956093 · outbound

This paper cites A Data-Driven Solution for The Cold Start Problem in Biomedical Image Classification.

In-context learning for medical image segmentation A Data-Driven Solution for The Cold Start Problem in Biomedical Image Classification

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:18:09.937373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 9123d17c-2290-4e50-95ca-3ee94c8e2897 · inbound

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation cites this paper.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation In-context learning for medical image segmentation

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:34:47.445174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.