Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images | Paper Citation Record

Observation 3b3fc64d-d000-4f60-8332-d8a6a81072a7 · outbound

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

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images U-net: Convolutional networks for biomedical image segmentation,

Reference 1

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Observation 29885caf-13c7-4431-a684-2fe622503954 · outbound

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

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Unet++: A nested u-net architecture for medical image segmenta- tion,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.327656Z

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Observation 3c1996ea-3cfa-41e1-854a-b990e0d8a4ac · outbound

This paper cites Small sample image segmen- tation by coupling convolutions and transformers,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Small sample image segmen- tation by coupling convolutions and transformers,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.319686Z

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Observation b155e949-3e7b-4841-a1a5-a39b638564da · outbound

This paper cites Erdunet: An efficient residual double- coding unet for medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Erdunet: An efficient residual double- coding unet for medical image segmentation,

Reference 4

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Observation 57a5687d-6ac4-4be4-b3a2-743574da4b13 · outbound

This paper cites Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.304613Z

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Observation 1d0bb689-5a86-4180-8708-068ba00fd6b2 · outbound

This paper cites H2former: An efficient hierarchical hybrid transformer for medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images H2former: An efficient hierarchical hybrid transformer for medical image segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.295734Z

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Observation b0183d9a-1b42-4874-897d-8d8edaf43388 · outbound

This paper cites Cmu-net: a strong convmixer-based medical ultrasound image segmentation network,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Cmu-net: a strong convmixer-based medical ultrasound image segmentation network,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.286580Z

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Observation 2a7a7ceb-c515-4e43-99f8-ef84b2627394 · outbound

This paper cites A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images A novel deep learning framework for automatic recognition of thyroid gland and tissues of neck in ultrasound image,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.277927Z

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Observation cd29d940-e176-4e76-a892-4186363228ec · outbound

This paper cites Ultrasound nodule segmentation using asymmetric learning with simple clinical annotation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Ultrasound nodule segmentation using asymmetric learning with simple clinical annotation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.268996Z

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Observation 7b67a468-aa3a-4e6d-942f-3aa31093ea34 · outbound

This paper cites Detection of lines and boundaries in speckle images-application to medical ultrasound,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Detection of lines and boundaries in speckle images-application to medical ultrasound,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.260260Z

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Observation dbffc09a-5f0f-4de2-ac8d-19016c4701f9 · outbound

This paper cites Performance analysis of speckle ultrasound image filtering,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Performance analysis of speckle ultrasound image filtering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.251991Z

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Observation 008c4e52-6678-45b4-b156-8b7f17514527 · outbound

This paper cites Interactive segmentation of medical images through fully convolutional neural networks.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Interactive segmentation of medical images through fully convolutional neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.785560Z

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Observation 5063a1d8-b486-411a-8df7-4ef7e761745f · outbound

This paper cites Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:39:08.993329Z

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Observation 85a757d4-e556-4801-928f-5bad85600029 · outbound

This paper cites Deepigeos: a deep interactive geodesic framework for medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Deepigeos: a deep interactive geodesic framework for medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.244226Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f871cf79-5129-42dd-9652-c91de7028415 · outbound

This paper cites MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

Reference 15

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Observation 575ffbcb-bfde-4e19-a2dd-8f9359e51e37 · outbound

This paper cites SAM-Med2D.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images SAM-Med2D

Reference 16

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Observation 8c55d480-d06f-4952-a873-a726d429ae5a · outbound

This paper cites Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.236897Z

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Observation a7d77b48-1a5d-4674-8b20-fe545f17c79d · outbound

This paper cites an unresolved cited work.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.751118Z

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Observation 72375e1a-fe67-475f-bc23-2c927b3b7f1a · outbound

This paper cites DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.800114Z

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Observation 3a00a267-4340-4c89-aae2-5e71e2b05e19 · outbound

This paper cites Segment anything in medical images,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Segment anything in medical images,

Reference 20

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Observation 4329da16-5544-45d9-807a-c3c15cbe702f · outbound

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

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.804664Z

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Observation 7772d1c0-2489-4ccc-9ac9-5c4baafca730 · outbound

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

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Customized Segment Anything Model for Medical Image Segmentation

Reference 22

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Observation 99473631-eb2f-445f-936c-746a4db315fc · outbound

This paper cites Evidential deep learning to quantify classification uncertainty,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Evidential deep learning to quantify classification uncertainty,

Reference 23

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Observation 80d23535-81a8-4ad5-a300-8e3af7d642bb · outbound

This paper cites Dirichlet-based Uncertainty Calibration for Active Domain Adaptation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Dirichlet-based Uncertainty Calibration for Active Domain Adaptation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.811917Z

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Observation 983cda0a-5283-4d42-bb63-0268b19d3aa1 · outbound

This paper cites Interactive graph cuts for optimal bound- ary & region segmentation of objects in nd images,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Interactive graph cuts for optimal bound- ary & region segmentation of objects in nd images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.215695Z

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Observation ec061423-6098-4faa-96e9-e6c1954894d8 · outbound

This paper cites Random walks for image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Random walks for image segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.206636Z

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Observation 7294615a-0b5c-4dab-9cc9-be99521b6fef · outbound

This paper cites Geodesic star convexity for interactive image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Geodesic star convexity for interactive image segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.198039Z

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Observation 5ab91e43-9f13-447a-bf14-7a0feee98409 · outbound

This paper cites ” grabcut.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images ” grabcut

Reference 28

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Observation 8a1a16c8-52fd-459b-8b83-ed47218f5d12 · outbound

This paper cites Phiseg: Capturing uncertainty in medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Phiseg: Capturing uncertainty in medical image segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.180349Z

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Observation a8a09688-d4c8-4ee7-903a-c3705c393c1b · outbound

This paper cites Semi-supervised npc segmentation with uncertainty and attention guided consistency,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Semi-supervised npc segmentation with uncertainty and attention guided consistency,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.173675Z

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Observation c76e6a7d-c1c1-4245-a4db-7c5ec60d0787 · outbound

This paper cites A probabilistic u-net for segmentation of ambiguous images,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images A probabilistic u-net for segmentation of ambiguous images,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.166341Z

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Observation e17e1d72-dc89-409d-b324-4de19ede3706 · outbound

This paper cites Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.158432Z

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Observation 37713d04-dad5-4c50-9f91-c9a151bffd47 · outbound

This paper cites Unified medical image segmentation by learning from uncertainty in an end- to-end manner,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Unified medical image segmentation by learning from uncertainty in an end- to-end manner,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.148600Z

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Observation e5736930-8c31-4417-b451-5a0eb584fdca · outbound

This paper cites Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.137420Z

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Observation b8df23e6-0402-4145-a469-4740730a2bb0 · outbound

This paper cites Uncertainty-aware hierarchical aggregation network for medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Uncertainty-aware hierarchical aggregation network for medical image segmentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.127165Z

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Observation cc48bd94-a369-4955-b4b4-aca2b2e0868b · outbound

This paper cites Towards fewer annotations: Active learning via region impurity and prediction uncer- tainty for domain adaptive semantic segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Towards fewer annotations: Active learning via region impurity and prediction uncer- tainty for domain adaptive semantic segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.115782Z

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Observation a63a1d8b-78b3-430e-9eb2-13b49c0a8f2a · outbound

This paper cites An uncertainty-guided tiered self-training framework for active source-free domain adaptation in prostate segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images An uncertainty-guided tiered self-training framework for active source-free domain adaptation in prostate segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.108271Z

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Observation c615d6a0-96e6-49ff-a7e6-9cb9761b1ba4 · outbound

This paper cites Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.100934Z

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Observation 1f2e60f4-8794-40a7-a4f7-c08d1bd9d386 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 39

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Observation a7afc2ea-2cb0-4054-b6fc-06577a05ff25 · outbound

This paper cites Confidence calibration and predictive uncertainty estimation for deep medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Confidence calibration and predictive uncertainty estimation for deep medical image segmentation,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.851494Z

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Observation fde55bcf-0ef0-473c-859a-bb9a78f3677a · outbound

This paper cites Asymmetric ensemble of asymmetric u-net models for brain tumor segmentation with uncertainty estimation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Asymmetric ensemble of asymmetric u-net models for brain tumor segmentation with uncertainty estimation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.084439Z

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Observation a4da90bd-50da-44d3-b9eb-36fb993276ee · outbound

This paper cites Evidence-based uncertainty-aware semi- supervised medical image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Evidence-based uncertainty-aware semi- supervised medical image segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.075738Z

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Observation 42df2326-6b76-4853-9e37-9248584f9575 · outbound

This paper cites EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.859588Z

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Observation 632346fc-2b10-42a5-b544-02c5cdaddf4a · outbound

This paper cites DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:39:08.933208Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1fc67eb6-4945-4cd3-a55e-68176736b36a · outbound

This paper cites An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.866437Z

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Observation 3df35055-8d26-41a4-ad65-5588e215d760 · outbound

This paper cites Tbrats: Trusted brain tumor segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Tbrats: Trusted brain tumor segmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.067160Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 91626ea9-a7d3-4d97-aa1a-6a18982b9282 · outbound

This paper cites A generalization of bayesian inference,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images A generalization of bayesian inference,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.059369Z

Source-reported events for the cited work

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Observation 464293e1-f306-423c-9b21-2f5eddd5a0fa · outbound

This paper cites Jsang, Subjective Logic: A formalism for reasoning under uncertainty.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Jsang, Subjective Logic: A formalism for reasoning under uncertainty

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.051802Z

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Observation 9e857400-2c06-4bd3-af4f-635a6dc3efd6 · outbound

This paper cites Dataset of breast ultrasound images,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Dataset of breast ultrasound images,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:08.874592Z

Source-reported events for the cited work

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Observation 08450628-4bf9-418e-b5eb-6ef97e7789a7 · outbound

This paper cites An open access thyroid ultrasound image database,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images An open access thyroid ultrasound image database,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.040338Z

Source-reported events for the cited work

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Observation 1ae77379-2932-4c1e-9c72-83333f141009 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Pytorch: An imperative style, high- performance deep learning library,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.016533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e4cccaca-d8fe-4e45-bf89-2ab4f26cd67c · outbound

This paper cites Video-based ai for beat-to-beat assessment of cardiac function,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Video-based ai for beat-to-beat assessment of cardiac function,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:39:09.024998Z

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

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 53

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This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 54

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This paper cites Segment anything,.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Segment anything,

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Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Unresolved cited work

Reference 9287

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