Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models | Paper Citation Record

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This paper cites Data-driven organic solubility prediction at the limit of aleatoric uncertainty.Nature Communications, 16(1):7497.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Data-driven organic solubility prediction at the limit of aleatoric uncertainty.Nature Communications, 16(1):7497

Reference 1

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Observation 7e115720-66d8-43b9-8c03-18b0896f0b51 · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Foundation models defining a new era in vision: a survey and outlook

Reference 2

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This paper cites The liver tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models The liver tumor segmentation benchmark (lits).Medical Image Analysis, 84:102680

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Observation 30ae2b8a-fad8-4467-9fba-25db23ac1e27 · outbound

This paper cites Learning sample difficulty from pre-trained models for reliable prediction.Advances in Neural Information Process- ing Systems, 36:25390–25408.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Learning sample difficulty from pre-trained models for reliable prediction.Advances in Neural Information Process- ing Systems, 36:25390–25408

Reference 4

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Observation 14b252fc-9f69-4893-a4ee-897d87503ae1 · outbound

This paper cites Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:00.254346Z

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Observation 1a1d1210-8e89-4d36-88b5-0a1d8db729c7 · outbound

This paper cites Uncertainty estimation by fisher information-based evidential deep learning.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Uncertainty estimation by fisher information-based evidential deep learning

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Observation 5a8b3312-b002-4232-8d69-48e9c1bda9ff · outbound

This paper cites Segvol: Universal and interactive volumetric medical image segmen- tation.Advances in Neural Information Processing Systems, 37:110746–110783.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Segvol: Universal and interactive volumetric medical image segmen- tation.Advances in Neural Information Processing Systems, 37:110746–110783

Reference 7

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Observation 770fe834-8fc8-4cad-b294-d0c481ff28ee · outbound

This paper cites Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d med- ical image segmentation.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d med- ical image segmentation

Reference 8

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Observation a9c269c9-90ea-48a4-9b8d-179519341eec · outbound

This paper cites The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 9

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Observation 3475c1d9-c7b8-4e36-b580-1a09a5f6e57c · outbound

This paper cites A review of uncertainty quantification in medical image anal- ysis: Probabilistic and non-probabilistic methods.Medical Image Analysis, 97:103223.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models A review of uncertainty quantification in medical image anal- ysis: Probabilistic and non-probabilistic methods.Medical Image Analysis, 97:103223

Reference 10

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Observation 9dfb0b00-107b-4133-add9-3b392cce3f6b · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models nnu-net: a self-configuring method for deep learning-based biomedical image segmen- tation.Nature methods, 18(2):203–211

Reference 11

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Observation db70837b-2391-4e2b-b4d5-cd80789be9d6 · outbound

This paper cites Uncertainty-guided learning for im- proving image manipulation detection.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Uncertainty-guided learning for im- proving image manipulation detection

Reference 12

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Observation 17ba5590-40f6-4cf5-b755-f0d9857b3b2d · outbound

This paper cites Deep learning in visual tracking: A review.IEEE transactions on neural networks and learning systems, 34 (9):5497–5516.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Deep learning in visual tracking: A review.IEEE transactions on neural networks and learning systems, 34 (9):5497–5516

Reference 13

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Observation f9ec667c-edba-4a78-a719-7678e5a66f46 · outbound

This paper cites The new generation brain-inspired sparse learn- ing: A comprehensive survey.IEEE Transactions on Artifi- cial Intelligence, 3(6):887–907.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models The new generation brain-inspired sparse learn- ing: A comprehensive survey.IEEE Transactions on Artifi- cial Intelligence, 3(6):887–907

Reference 14

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Observation f8500048-ea85-484e-a1f5-ed975b4594fe · outbound

This paper cites Ai meets physics: a comprehensive survey.Artificial Intelligence Review, 57(9):256.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Ai meets physics: a comprehensive survey.Artificial Intelligence Review, 57(9):256

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Observation 83a09e72-9dcb-4ea5-84e2-c32d898de041 · outbound

This paper cites Multiscale deep learning for detection and recognition: A comprehen- sive survey.IEEE Transactions on Neural Networks and Learning Systems.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Multiscale deep learning for detection and recognition: A comprehen- sive survey.IEEE Transactions on Neural Networks and Learning Systems

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Observation 7f4a8089-b13a-46ac-863a-749f3955e149 · outbound

This paper cites Foundation models meet medical image interpretation.Research, 9:1024.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Foundation models meet medical image interpretation.Research, 9:1024

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Observation dc23f713-f313-49dd-b5a2-4080ae2d9d38 · outbound

This paper cites Url: A representation learning bench- mark for transferable uncertainty estimates.Advances in Neural Information Processing Systems, 36:13956–13980.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Url: A representation learning bench- mark for transferable uncertainty estimates.Advances in Neural Information Processing Systems, 36:13956–13980

Reference 18

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Observation 358f0edb-baff-4673-aea4-fd6bcbc3d719 · outbound

This paper cites Dht-net: Dy- namic hierarchical transformer network for liver and tumor segmentation.IEEE Journal of Biomedical and Health In- formatics.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Dht-net: Dy- namic hierarchical transformer network for liver and tumor segmentation.IEEE Journal of Biomedical and Health In- formatics

Reference 19

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Unsupervised few-shot image classification by learning features into clustering space

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Observation 3dcfee87-0633-4d7e-9d4a-651ead78c8aa · outbound

This paper cites Minent: Minimum entropy for self-supervised representation learning.Pattern Recognition, 138:109364.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Minent: Minimum entropy for self-supervised representation learning.Pattern Recognition, 138:109364

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Observation 2a9cb6f6-07bb-4ef8-b232-f34e85641d48 · outbound

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Clip-driven universal model for organ segmentation and tumor detection

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Observation e2e51a18-7089-4d61-857b-5e8b50e81b7f · outbound

This paper cites Bio-inspired multi- scale contourlet attention networks.IEEE Transactions on Multimedia.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Bio-inspired multi- scale contourlet attention networks.IEEE Transactions on Multimedia

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Observation f1794b13-fbf6-4946-8202-311af68876df · outbound

This paper cites Biomedical foun- dation model: A survey.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Biomedical foun- dation model: A survey

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Observation 1e5a2e6a-2617-475a-82c6-61f3ab98367f · outbound

This paper cites Word: A large scale dataset, benchmark and clinical applicable study for abdom- inal organ segmentation from ct image.Medical Image Anal- ysis, 82:102642.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Word: A large scale dataset, benchmark and clinical applicable study for abdom- inal organ segmentation from ct image.Medical Image Anal- ysis, 82:102642

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Observation 3a7bb167-b75d-4696-8e25-b5c267eec878 · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

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Observation 6888944a-6714-4736-8ce1-a08c45cb69aa · outbound

This paper cites MedSAM2: Segment Anything in 3D Medical Images and Videos.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models MedSAM2: Segment Anything in 3D Medical Images and Videos

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This paper cites Delving into Semantic Scale Imbalance.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Delving into Semantic Scale Imbalance

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Observation 6ccfc2a2-b892-4f33-9bb0-4fcacf501409 · outbound

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Unveiling and mitigating generalized biases of dnns through the intrinsic dimensions of perceptual manifolds.IEEE Transactions on Pattern Analysis and Machine Intelligence

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This paper cites Predicting and enhancing the fairness of dnns with the curva- ture of perceptual manifolds.IEEE Transactions on Pattern Analysis and Machine Intelligence.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Predicting and enhancing the fairness of dnns with the curva- ture of perceptual manifolds.IEEE Transactions on Pattern Analysis and Machine Intelligence

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This paper cites Foundation models for generalist medi- cal artificial intelligence.Nature, 616(7956):259–265.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Foundation models for generalist medi- cal artificial intelligence.Nature, 616(7956):259–265

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Benchmarking uncertainty disentanglement: Specialized un- certainties for specialized tasks.Advances in neural infor- mation processing systems, 37:50972–51038

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Deep learning on a data diet: Finding important ex- amples early in training.Advances in neural information processing systems, 34:20596–20607

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset.Scien- tific data, 8(1):167

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Angular gap: Reducing the uncertainty of image difficulty through model calibration

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Beyond neural scaling laws: beat- ing power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Recurrent connectivity supports higher- level visual and semantic object representations in the brain

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models To- talsegmentator: robust segmentation of 104 anatomic struc- tures in ct images.Radiology: Artificial Intelligence, 5(5): e230024

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Observation 457f3b90-0db3-4d6e-872b-5a9ccea47a13 · outbound

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models 3d medical image segmentation using parallel trans- formers.Pattern Recognition, 138:109432

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Observation 6238fa06-a92b-4ed4-a95f-ca9a2d3560f2 · outbound

This paper cites Structural uncertainty estima- tion for medical image segmentation.Medical Image Analy- sis, page 103602.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Structural uncertainty estima- tion for medical image segmentation.Medical Image Analy- sis, page 103602

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Ept-net: Edge perception trans- former for 3d medical image segmentation.IEEE Transac- tions on Medical Imaging

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Observation 898dfb9a-b199-4f9e-b353-2c04ca621b7d · outbound

This paper cites One step closer to unbiased aleatoric uncertainty estimation.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models One step closer to unbiased aleatoric uncertainty estimation

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Observation bbb36bfc-2301-4de4-afb4-fab58d8dec29 · outbound

This paper cites Rethinking epis- temic and aleatoric uncertainty for active open-set annota- tion: An energy-based approach.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Rethinking epis- temic and aleatoric uncertainty for active open-set annota- tion: An energy-based approach

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