EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation | Paper Citation Record

Observation 31f5840e-3a03-42f3-8727-f54942336d49 · outbound

This paper cites Dmsps: Dynamically mixed soft pseudo-label supervision for scribble-supervised medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Dmsps: Dynamically mixed soft pseudo-label supervision for scribble-supervised medical image segmentation,

Reference 1

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This paper cites Pa-seg: Learning from point annotations for 3d medical image segmentation using contextual regularization and cross knowledge distillation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Pa-seg: Learning from point annotations for 3d medical image segmentation using contextual regularization and cross knowledge distillation,

Reference 2

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This paper cites Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images,

Reference 3

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This paper cites Weakly supervised brain lesion segmentation via attentional representation learn- ing,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Weakly supervised brain lesion segmentation via attentional representation learn- ing,

Reference 4

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This paper cites Scribblesup: Scribble- supervised convolutional networks for semantic segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Scribblesup: Scribble- supervised convolutional networks for semantic segmentation,

Reference 5

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This paper cites Deepcut: Object segmentation from bounding box annotations using convolutional neural networks,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Deepcut: Object segmentation from bounding box annotations using convolutional neural networks,

Reference 6

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This paper cites Inter extreme points geodesics for end-to-end weakly supervised image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Inter extreme points geodesics for end-to-end weakly supervised image segmentation,

Reference 7

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This paper cites Scribblevc: Scribble- supervised medical image segmentation with vision-class embedding,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Scribblevc: Scribble- supervised medical image segmentation with vision-class embedding,

Reference 8

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This paper cites Blpseg: Balance the label preference in scribble-supervised semantic segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Blpseg: Balance the label preference in scribble-supervised semantic segmentation,

Reference 9

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Observation fce3f9a6-b9b9-4a55-be52-b5ab2210658a · outbound

This paper cites Sparsely annotated semantic segmentation with adaptive gaussian mixtures,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Sparsely annotated semantic segmentation with adaptive gaussian mixtures,

Reference 10

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Observation ac8888a1-d8b9-4089-b283-684f6a11626b · outbound

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Unresolved cited work

Reference 11

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This paper cites Kvasir-seg: A segmented polyp dataset,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Kvasir-seg: A segmented polyp dataset,

Reference 12

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

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,

Reference 13

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This paper cites Learning deep features for discriminative localization,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Learning deep features for discriminative localization,

Reference 14

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This paper cites Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification,

Reference 15

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This paper cites Weakly supervised instance segmentation using the bounding box tightness prior,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Weakly supervised instance segmentation using the bounding box tightness prior,

Reference 16

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This paper cites Learning to segment from scribbles using multi-scale adversarial attention gates,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Learning to segment from scribbles using multi-scale adversarial attention gates,

Reference 17

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This paper cites Scribble-based hierarchical weakly supervised learning for brain tumor segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Scribble-based hierarchical weakly supervised learning for brain tumor segmentation,

Reference 18

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This paper cites Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,

Reference 19

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This paper cites Simclr: A simple framework for contrastive learning of visual representations,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Simclr: A simple framework for contrastive learning of visual representations,

Reference 20

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This paper cites A survey on contrastive self-supervised learning,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation A survey on contrastive self-supervised learning,

Reference 21

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This paper cites Rethinking semi-supervised medical image segmentation: A variance-reduction perspective,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Rethinking semi-supervised medical image segmentation: A variance-reduction perspective,

Reference 22

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This paper cites Exploring cross-image pixel contrast for semantic segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Exploring cross-image pixel contrast for semantic segmentation,

Reference 23

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This paper cites Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation,

Reference 24

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

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 25

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This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 26

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This paper cites Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,

Reference 27

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This paper cites Exploring feature representation learning for semi-supervised medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Exploring feature representation learning for semi-supervised medical image segmentation,

Reference 28

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This paper cites Contrastive registra- tion for unsupervised medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Contrastive registra- tion for unsupervised medical image segmentation,

Reference 29

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,

Reference 30

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This paper cites Transfuse: Fusing transformers and cnns for medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Transfuse: Fusing transformers and cnns for medical image segmentation,

Reference 31

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Observation 9f9d59bc-961a-4b9c-8644-7d8057042f2d · outbound

This paper cites Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,

Reference 32

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Normal- ized cut loss for weakly-supervised cnn segmentation,

Reference 33

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation

Reference 34

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Gated CRF Loss for Weakly Supervised Semantic Image Segmentation

Reference 35

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This paper cites Mumford–shah loss functional for image seg- mentation with deep learning,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Mumford–shah loss functional for image seg- mentation with deep learning,

Reference 36

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Observation 3ecd7b5e-1bf7-4c24-8e74-5003a16f1daa · outbound

This paper cites Weakly supervised segmentation of covid19 infection with scribble annotation on ct images,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Weakly supervised segmentation of covid19 infection with scribble annotation on ct images,

Reference 37

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This paper cites Trimix: A general framework for medical image segmentation from limited supervision,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Trimix: A general framework for medical image segmentation from limited supervision,

Reference 38

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

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 39

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,

Reference 40

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This paper cites ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

Reference 41

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Deep residual learning for image recognition,

Reference 42

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This paper cites Consistency and adversarial semi-supervised learning for medical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Consistency and adversarial semi-supervised learning for medical image segmentation,

Reference 43

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This paper cites Cross-level contrastive learning and consistency constraint for semi-supervised med- ical image segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Cross-level contrastive learning and consistency constraint for semi-supervised med- ical image segmentation,

Reference 44

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Observation 3e4bfb8b-8644-4e3a-a2c0-cfe1bf477f8e · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 45

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation St++: Make self- training work better for semi-supervised semantic segmentation,

Reference 46

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Observation 96cf934c-8b94-4a62-a821-4fab294dd687 · outbound

This paper cites Semi- supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Semi- supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank,

Reference 47

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This paper cites Multi-level attention network for retinal vessel segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Multi-level attention network for retinal vessel segmentation,

Reference 48

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This paper cites Boxsup: Exploiting bounding boxes to super- vise convolutional networks for semantic segmentation,.

EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Boxsup: Exploiting bounding boxes to super- vise convolutional networks for semantic segmentation,

Reference 49

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EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation Unresolved cited work

Reference 2020

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