Out-of-distribution data supervision towards biomedical semantic segmentation | Paper Citation Record

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This paper cites Hubmap-hacking the kidney,.

Out-of-distribution data supervision towards biomedical semantic segmentation Hubmap-hacking the kidney,

Reference 1

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This paper cites Revisiting neural scaling laws in language and vision,.

Out-of-distribution data supervision towards biomedical semantic segmentation Revisiting neural scaling laws in language and vision,

Reference 2

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Observation b8f1050f-1aa2-451b-8508-c683990183af · outbound

This paper cites Deep semantic segmentation of natural and medical images: a review,.

Out-of-distribution data supervision towards biomedical semantic segmentation Deep semantic segmentation of natural and medical images: a review,

Reference 3

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This paper cites Out-distribution aware Self-training in an Open World Setting.

Out-of-distribution data supervision towards biomedical semantic segmentation Out-distribution aware Self-training in an Open World Setting

Reference 4

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This paper cites Medical image segmentation via unsupervised convolutional neural network,.

Out-of-distribution data supervision towards biomedical semantic segmentation Medical image segmentation via unsupervised convolutional neural network,

Reference 5

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Observation 3eaf72ae-c17f-4a90-893d-9e3af81ef317 · outbound

This paper cites The Value of Out-of-Distribution Data.

Out-of-distribution data supervision towards biomedical semantic segmentation The Value of Out-of-Distribution Data

Reference 6

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

Out-of-distribution data supervision towards biomedical semantic segmentation Imagenet: A large scale hierarchical image database,

Reference 7

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This paper cites Reducing network agnostophobia,.

Out-of-distribution data supervision towards biomedical semantic segmentation Reducing network agnostophobia,

Reference 8

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Observation a989a78b-f981-49b6-8ae3-fc244e0075b5 · outbound

This paper cites Ma-net: A multi -scale attention network for liver and tumor segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Ma-net: A multi -scale attention network for liver and tumor segmentation,

Reference 9

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Observation 65636078-c5e1-49aa-a43a-349c02b7d6bd · outbound

This paper cites Improving the segmentation of anatomical structures in chest radiographs using u -net with an imagenet pre -trained encoder,.

Out-of-distribution data supervision towards biomedical semantic segmentation Improving the segmentation of anatomical structures in chest radiographs using u -net with an imagenet pre -trained encoder,

Reference 10

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Observation 427aa0f6-f3f3-4564-abe7-4805d968d3ba · outbound

This paper cites Pannuke: an open pan -cancer histology dataset for nuclei instance segmentation and classification ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Pannuke: an open pan -cancer histology dataset for nuclei instance segmentation and classification ,

Reference 11

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Observation 77b6baf4-a260-4c5a-b428-c4742b3820f0 · outbound

This paper cites Are vision transformers robust to spurious correlations?.

Out-of-distribution data supervision towards biomedical semantic segmentation Are vision transformers robust to spurious correlations?

Reference 12

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Observation aa2a348e-8304-4187-b432-0f2acce16667 · outbound

This paper cites Lizard: A large -scale dataset for colonic nuclear instance segmentation and classification ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Lizard: A large -scale dataset for colonic nuclear instance segmentation and classification ,

Reference 13

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Observation 57fc5caf-489b-4629-8075-8589540d9749 · outbound

This paper cites Hover-net: Simultaneous segmentation and classification of nuclei in multitissue histology images,.

Out-of-distribution data supervision towards biomedical semantic segmentation Hover-net: Simultaneous segmentation and classification of nuclei in multitissue histology images,

Reference 14

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Observation 0390491d-a68d-40f9-a3aa-81ada9a16eaf · outbound

This paper cites Unsupervised microvascular image segmentation using an active contours mimicking neural network,.

Out-of-distribution data supervision towards biomedical semantic segmentation Unsupervised microvascular image segmentation using an active contours mimicking neural network,

Reference 15

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Observation 4031eab1-ea5b-4f09-b997-b886ec58a734 · outbound

This paper cites Deep convolutional neural networks for segmenting 3d in vivo multiphoton images of vasculature in alzheimer disease mouse models,.

Out-of-distribution data supervision towards biomedical semantic segmentation Deep convolutional neural networks for segmenting 3d in vivo multiphoton images of vasculature in alzheimer disease mouse models,

Reference 16

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Observation e2355e98-1b34-4dfc-ba89-24e3685178b0 · outbound

This paper cites Model rubik’s cube: Twisting resolution, depth and width for tinynets,.

Out-of-distribution data supervision towards biomedical semantic segmentation Model rubik’s cube: Twisting resolution, depth and width for tinynets,

Reference 17

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Observation 4471d6f4-ffdb-4d2c-9f81-cfa419520e95 · outbound

This paper cites Deep residual learning for image recognition,.

Out-of-distribution data supervision towards biomedical semantic segmentation Deep residual learning for image recognition,

Reference 18

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Observation eefa358e-8edd-4c60-bb1d-7ac3f164234f · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Out-of-distribution data supervision towards biomedical semantic segmentation A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 19

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This paper cites Deep Anomaly Detection with Outlier Exposure.

Out-of-distribution data supervision towards biomedical semantic segmentation Deep Anomaly Detection with Outlier Exposure

Reference 20

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Observation 77f2be34-2ea5-49f9-8315-368bf1e38e80 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out -of-distribution data ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Generalized odin: Detecting out-of-distribution image without learning from out -of-distribution data ,

Reference 21

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Observation 4e7d5216-9b07-449f-a8c1-5ca1f3b51f05 · outbound

This paper cites Densely connected convolutional networks ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Densely connected convolutional networks ,

Reference 22

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Observation a6689e94-041b-405a-a8aa-67e2c69e3da1 · outbound

This paper cites TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation.

Out-of-distribution data supervision towards biomedical semantic segmentation TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

Reference 23

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Observation 7068f463-d7f3-41dd-88c6-3e33c1d9152d · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Out-of-distribution data supervision towards biomedical semantic segmentation Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 24

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Observation d1e79baf-3f6d-4ba3-b2a9-65c28de15dcb · outbound

This paper cites Data-Centric Artificial Intelligence.

Out-of-distribution data supervision towards biomedical semantic segmentation Data-Centric Artificial Intelligence

Reference 25

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Observation 59e7249a-4edb-4575-b8cb-e213aff65e2b · outbound

This paper cites Scaling Laws for Neural Language Models.

Out-of-distribution data supervision towards biomedical semantic segmentation Scaling Laws for Neural Language Models

Reference 26

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This paper cites Scaling Laws For Deep Learning Based Image Reconstruction.

Out-of-distribution data supervision towards biomedical semantic segmentation Scaling Laws For Deep Learning Based Image Reconstruction

Reference 27

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Observation 56b191d1-9869-4987-9eda-bb7cc3a2629d · outbound

This paper cites A multi-organ nucleus segmentation challenge,.

Out-of-distribution data supervision towards biomedical semantic segmentation A multi-organ nucleus segmentation challenge,

Reference 28

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Observation b05a1af8-dcb8-4f0c-9209-eb100ff520f5 · outbound

This paper cites Weakly supervised semantic segmentation using out- of-distribution data,.

Out-of-distribution data supervision towards biomedical semantic segmentation Weakly supervised semantic segmentation using out- of-distribution data,

Reference 29

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Observation cca87778-44c7-4a1e-9113-14062b4a5d24 · outbound

This paper cites Removing undesirable feature contributions using out-of- distribution data,.

Out-of-distribution data supervision towards biomedical semantic segmentation Removing undesirable feature contributions using out-of- distribution data,

Reference 30

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This paper cites Analyzing overfitting under class imbalance in neural networks for image segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Analyzing overfitting under class imbalance in neural networks for image segmentation,

Reference 31

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Observation 7ecacd8e-a224-4c72-889b-6dc82e54ee7e · outbound

This paper cites Cam-unet: Class activation map guided unet with feedback refinement for defect segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Cam-unet: Class activation map guided unet with feedback refinement for defect segmentation,

Reference 32

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Observation f6e80c5f-1da8-4121-a21c-05d9f2110c04 · outbound

This paper cites Microsoft coco: Common objects in context ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Microsoft coco: Common objects in context ,

Reference 33

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Observation 7785d7e7-85c0-455e-854a-e5cd77546ea5 · outbound

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

Out-of-distribution data supervision towards biomedical semantic segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 34

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Observation b337d26b-2454-42f5-86d9-27baa4a62022 · outbound

This paper cites On the impact of spurious correlation for out -of-distribution detection ,.

Out-of-distribution data supervision towards biomedical semantic segmentation On the impact of spurious correlation for out -of-distribution detection ,

Reference 35

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Observation ea5f247d-01a8-42cb-8103-776526cf0621 · outbound

This paper cites Bridging the gap between natural and medical images through deep colorization,.

Out-of-distribution data supervision towards biomedical semantic segmentation Bridging the gap between natural and medical images through deep colorization,

Reference 36

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Observation b8610549-c55d-4002-b36d-1f24769b9b5b · outbound

This paper cites Towards a guideline for evaluation metrics in medical image segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Towards a guideline for evaluation metrics in medical image segmentation,

Reference 37

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Observation 2827b4cd-bdba-414e-8be1-8cead973f5f8 · outbound

This paper cites Universal lesion detection and classification using limited data and weakly -supervised self -training,.

Out-of-distribution data supervision towards biomedical semantic segmentation Universal lesion detection and classification using limited data and weakly -supervised self -training,

Reference 38

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Observation a6f642e1-fccd-4031-aa97-3171a4a0ef26 · outbound

This paper cites Medical image segmentation with limited supervision: A review of deep network models,.

Out-of-distribution data supervision towards biomedical semantic segmentation Medical image segmentation with limited supervision: A review of deep network models,

Reference 39

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Observation 1e485a67-b588-4c52-940e-762b8edbcb77 · outbound

This paper cites Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers.

Out-of-distribution data supervision towards biomedical semantic segmentation Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Reference 40

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Observation b622a76e-bce4-410a-9f5e-3bbcf9c12fc1 · outbound

This paper cites Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation.

Out-of-distribution data supervision towards biomedical semantic segmentation Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation

Reference 41

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Observation e6149457-f989-4e68-9ba0-befd4426ffd2 · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Generalized intersection over union: A metric and a loss for bounding box regression ,

Reference 42

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Observation 432c0842-76fd-40fb-92e3-ac3f613da77c · outbound

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

Out-of-distribution data supervision towards biomedical semantic segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:27.102388Z

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Observation 9f38b8c0-d046-4398-9d11-7115d2dccf2e · outbound

This paper cites The Elephant in the Room.

Out-of-distribution data supervision towards biomedical semantic segmentation The Elephant in the Room

Reference 44

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Observation e771842b-3808-4852-84ae-797db8ef085c · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Out-of-distribution data supervision towards biomedical semantic segmentation Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:26.900737Z

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Observation e8e5267b-09df-4627-8505-273139a20280 · outbound

This paper cites How reliable are out -of-distribution generalization methods for medical image segmentation?.

Out-of-distribution data supervision towards biomedical semantic segmentation How reliable are out -of-distribution generalization methods for medical image segmentation?

Reference 46

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Observation 4fb7e464-07f4-45b2-b239-46c7754bb1f8 · outbound

This paper cites Global healthcare fairness: We should be sharing more, not less, data,.

Out-of-distribution data supervision towards biomedical semantic segmentation Global healthcare fairness: We should be sharing more, not less, data,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:26.447255Z

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Observation eeb1eda2-286d-48d4-8264-3f892c9a17df · outbound

This paper cites Data-suite: Data -centric identification of in -distribution incongruous examples,.

Out-of-distribution data supervision towards biomedical semantic segmentation Data-suite: Data -centric identification of in -distribution incongruous examples,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:26.189981Z

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Observation c3529753-d339-4c29-8e4b-3820c0089167 · outbound

This paper cites Very deep convolutional networks for large -scale image recognition ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Very deep convolutional networks for large -scale image recognition ,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:25.878870Z

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Observation 27c5f291-254f-469c-9f08-7d89a52e499d · outbound

This paper cites Robustness to spurious correlations via human annotations ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Robustness to spurious correlations via human annotations ,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:25.670590Z

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Observation ca84e7e2-2c6e-454a-8a7b-333e73afb9cd · outbound

This paper cites Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations ,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:25.400787Z

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Observation 9d308c86-41f7-4b4e-b207-af7516c70a0e · outbound

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

Out-of-distribution data supervision towards biomedical semantic segmentation Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:25.120839Z

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Observation f5885e7f-ee5c-4e3d-9f42-0234978bbd3d · outbound

This paper cites Surrogate supervision for medical image analysis: Effective deep learning from limited quantities of labeled data ,.

Out-of-distribution data supervision towards biomedical semantic segmentation Surrogate supervision for medical image analysis: Effective deep learning from limited quantities of labeled data ,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:24.830452Z

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Observation cf3115be-16ec-43ac-b0df-0f3383799a41 · outbound

This paper cites Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:24.618355Z

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Observation d02d3ef0-2e5a-4f14-88ea-485620d2f514 · outbound

This paper cites Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation.

Out-of-distribution data supervision towards biomedical semantic segmentation Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:58:22.860211Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1697e199-a016-4bf8-9271-a098e3d14b33 · outbound

This paper cites MobileOne: An Improved One millisecond Mobile Backbone.

Out-of-distribution data supervision towards biomedical semantic segmentation MobileOne: An Improved One millisecond Mobile Backbone

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:58:22.680421Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d863a735-0279-4939-a7ca-317cd88ced91 · outbound

This paper cites Open-sampling: Exploring out -of-distribution data for re - balancing long-tailed datasets,.

Out-of-distribution data supervision towards biomedical semantic segmentation Open-sampling: Exploring out -of-distribution data for re - balancing long-tailed datasets,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:24.384595Z

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Observation 17460082-d1e6-4515-bbd5-6332ea103f77 · outbound

This paper cites Boosting dense long -tailed object detection from data -centric view,.

Out-of-distribution data supervision towards biomedical semantic segmentation Boosting dense long -tailed object detection from data -centric view,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:24.137573Z

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Observation 9a4c35c2-924b-45ab-954c-f18eb5eb137b · outbound

This paper cites Understanding rare spurious correlations in neural networks,.

Out-of-distribution data supervision towards biomedical semantic segmentation Understanding rare spurious correlations in neural networks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:23.860245Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 420d3694-d172-4937-9e24-cc2785e5f795 · outbound

This paper cites Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels.

Out-of-distribution data supervision towards biomedical semantic segmentation Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:58:22.501001Z

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Observation 60e92c14-a18c-49c4-94a8-892fcb7700f1 · outbound

This paper cites Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness.

Out-of-distribution data supervision towards biomedical semantic segmentation Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:58:22.279687Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4b683b9-1901-414a-b7b2-72bc46c6b63f · outbound

This paper cites Unet++: A nested u -net architecture for medical image segmentation,.

Out-of-distribution data supervision towards biomedical semantic segmentation Unet++: A nested u -net architecture for medical image segmentation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:58:23.646625Z

Source-reported events for the cited work

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