OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background | Paper Citation Record

Observation 0e11f332-3c05-445a-ae6f-0a35ac4da1d3 · outbound

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

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background U-net: Convolutional networks for biomedical image segmentation,

Reference 1

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Observation 22a8138e-3403-4b6f-955e-7d0dd8928ca7 · outbound

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

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 2

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Observation 5de35599-5504-4a4e-9e1e-fde1525eace7 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmenta- tion,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

Reference 3

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Observation 6753b77b-fbb3-406d-8b4c-b33b07d766b7 · outbound

This paper cites nn- former: V olumetric medical image segmentation via a 3d transformer,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background nn- former: V olumetric medical image segmentation via a 3d transformer,

Reference 4

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Observation 29bfc498-5940-4cdc-9504-1e86140e88b1 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Semi-supervised semantic segmentation with cross pseudo supervision,

Reference 5

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Observation 29027302-5a45-4628-b756-1d003f571172 · outbound

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

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 6

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Observation 50a6f11e-305e-42c4-92fd-4276d3420376 · outbound

This paper cites Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation,

Reference 7

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Observation b501d4e3-8bc6-425a-b622-4f868af99d0d · outbound

This paper cites Semi-supervised left atrium segmentation with mutual consistency training,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Semi-supervised left atrium segmentation with mutual consistency training,

Reference 8

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Observation 120bf413-d333-45fc-a37b-2c7e9815835b · outbound

This paper cites Mcf: Mutual correction framework for semi-supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Mcf: Mutual correction framework for semi-supervised medical image segmentation,

Reference 9

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Observation 052f45c6-a90d-4ede-8d5c-57faf722d834 · outbound

This paper cites Pseudo-labeling and confirmation bias in deep semi-supervised learn- ing,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Pseudo-labeling and confirmation bias in deep semi-supervised learn- ing,

Reference 10

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Observation cf45e709-2b6f-4455-b347-e0c99db71547 · outbound

This paper cites Disentangled repre- sentation learning in cardiac image analysis,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Disentangled repre- sentation learning in cardiac image analysis,

Reference 11

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Observation 3a455fb8-240f-4fd8-a06d-11c9e10caa7b · outbound

This paper cites Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation,

Reference 12

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Observation c44914d1-f86b-4bc8-b8b1-6c0f2badd243 · outbound

This paper cites Adaptfrcnet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Adaptfrcnet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation,

Reference 13

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Observation af9d2d11-eb2b-449b-8135-97bb42842c3c · outbound

This paper cites Bidirectional copy-paste for semi-supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Bidirectional copy-paste for semi-supervised medical image segmentation,

Reference 14

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Observation fa0e5340-ce95-4ea5-8df4-aaa483243995 · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Realistic evaluation of deep semi-supervised learning algorithms,

Reference 15

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Observation 20fbd494-ef2d-498a-8089-560070ba8fc2 · outbound

This paper cites When confidence fails: Revisiting pseudo-label selection in semi-supervised semantic segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background When confidence fails: Revisiting pseudo-label selection in semi-supervised semantic segmentation,

Reference 16

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Observation 37d6d867-168d-46e9-ab65-f82312d16839 · outbound

This paper cites Semi-supervised semantic segmen- tation with cross-consistency training,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Semi-supervised semantic segmen- tation with cross-consistency training,

Reference 17

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Observation 766bc021-ccfe-4fab-a98a-bc2a306b18f7 · outbound

This paper cites Semi-supervised medical image segmentation through dual-task consistency,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Semi-supervised medical image segmentation through dual-task consistency,

Reference 18

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Observation 646be943-98c8-4566-b366-8bfb46cb5354 · outbound

This paper cites Semi-supervised medical image segmentation using cross-model pseudo-supervision with shape aware- ness and local context constraints,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Semi-supervised medical image segmentation using cross-model pseudo-supervision with shape aware- ness and local context constraints,

Reference 19

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Observation beab2ff0-d020-43a9-a9f1-22b7cf773f1b · outbound

This paper cites Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency,

Reference 20

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Observation fe9edc14-5002-4f0b-8612-c82045493169 · outbound

This paper cites Shape-aware semi-supervised 3d seman- tic segmentation for medical images,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Shape-aware semi-supervised 3d seman- tic segmentation for medical images,

Reference 21

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Observation b22a3d70-39b9-44ae-96b7-200cf65b5cf8 · outbound

This paper cites St++: Make self- training work better for semi-supervised semantic segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background St++: Make self- training work better for semi-supervised semantic segmentation,

Reference 22

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Observation a3ae95e7-7bb9-4688-a37c-8db1a655a5d1 · outbound

This paper cites Learnable prompting sam-induced knowledge distillation for semi- supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Learnable prompting sam-induced knowledge distillation for semi- supervised medical image segmentation,

Reference 23

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Observation 5f984d9a-13fe-4f79-a564-b128ba33fd7f · outbound

This paper cites Allspark: Reborn labeled features from unlabeled in transformer for semi-supervised semantic segmenta- tion,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Allspark: Reborn labeled features from unlabeled in transformer for semi-supervised semantic segmenta- tion,

Reference 24

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Observation 8eab4329-8afa-45e9-9806-144f0584d6e4 · outbound

This paper cites A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation,

Reference 25

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Observation c77e8c82-8cd9-489e-8093-4057d204a370 · outbound

This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 26

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Observation 8ef75325-6ab0-447d-a06c-4760299c99d6 · outbound

This paper cites Daw: Exploring the better weighting function for semi-supervised semantic segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Daw: Exploring the better weighting function for semi-supervised semantic segmentation,

Reference 27

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Observation a0c0e094-daf0-42b0-9d64-0a6dc559ca2b · outbound

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

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Rethinking semi-supervised medical image segmentation: A variance-reduction perspective,

Reference 28

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Observation dbb9602c-a97a-4a6e-bcef-b47fe04326b8 · outbound

This paper cites Meta pseudo labels,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Meta pseudo labels,

Reference 29

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Observation 1f9e4b62-d2e6-436a-9362-d27d0f4cb284 · outbound

This paper cites Metassl: A general heterogeneous loss for semi-supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Metassl: A general heterogeneous loss for semi-supervised medical image segmentation,

Reference 30

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Observation ed9a7aaa-d2d8-47bb-839f-af8bb45ad9e3 · outbound

This paper cites In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

Reference 31

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Observation 0a5273a9-2788-4bb4-8533-f693b6dfeb7a · outbound

This paper cites Fixmatch: Simplifying semi- supervised learning with consistency and confidence,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Fixmatch: Simplifying semi- supervised learning with consistency and confidence,

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Observation 26df29d9-b021-4cfb-ba0f-cbbc36ca6d46 · outbound

This paper cites Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

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Observation f8bb2f11-8ffb-4746-9511-37c1dfa15c50 · outbound

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

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Kvasir-seg: A segmented polyp dataset,

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Observation 871be6b4-fd97-4bf4-ab7a-beaa7b418418 · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 35

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Observation a45d2401-711b-4e04-b49c-36d57426c3f5 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

Reference 36

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Observation e70dce97-0c48-4b5a-b359-63720c360a02 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 37

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This paper cites Dmt: Dynamic mutual training for semi-supervised learning,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Dmt: Dynamic mutual training for semi-supervised learning,

Reference 38

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Observation c0b2a1df-daff-43b7-8863-49d84f1732e4 · outbound

This paper cites Frcnet: Frequency and re- gion consistency for semi-supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Frcnet: Frequency and re- gion consistency for semi-supervised medical image segmentation,

Reference 39

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Observation 7cf31ed1-6bce-4b94-a9e9-b7f583f39b69 · outbound

This paper cites Exploring smoothness and class-separation for semi-supervised medical image segmentation,.

OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background Exploring smoothness and class-separation for semi-supervised medical image segmentation,

Reference 40

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