QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images | Paper Citation Record

Observation 9fb94a30-9d95-4146-9758-a09292d24efa · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images U-net: Convolutional networks for biomedical image segmentation

Reference 1

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Observation 731110d4-10ae-4e33-a268-ebc48ad52330 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Attention U-Net: Learning Where to Look for the Pancreas

Reference 2

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Observation ff6cdc54-dd57-4703-8c6b-5832a92e805e · outbound

This paper cites Unet++: A nested u-net architecture for medical imagesegmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Unet++: A nested u-net architecture for medical imagesegmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.432787Z

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Observation bb9daa42-983d-47b4-a90d-bbb1788e5431 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 4

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Observation 3409e5ca-a975-4060-9d67-257fa216ebd9 · outbound

This paper cites Rt-unet: an advanced network based on residual network and transformer for medical image segmentation.International Journal of Intelligent Systems, 37(11):8565–8582, 2022.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Rt-unet: an advanced network based on residual network and transformer for medical image segmentation.International Journal of Intelligent Systems, 37(11):8565–8582, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.418603Z

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Observation 3648f761-ab2c-4a02-b820-f0d880800db1 · outbound

This paper cites Transcunet: Unet cross fused transformer for medical image segmentation.Computers in Biology and Medicine, 150:106207, 2022.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Transcunet: Unet cross fused transformer for medical image segmentation.Computers in Biology and Medicine, 150:106207, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.403620Z

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Observation 5e00a9aa-3061-4d5c-8781-0f658bfa00a0 · outbound

This paper cites Nguyen-Tat, Thien-Qua T.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Nguyen-Tat, Thien-Qua T

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.388005Z

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Observation 199544f9-6645-4e21-99da-4a21e49ea4f2 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation aa1efbbb-bc52-456c-b0f8-873786e492d3 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.442577Z

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Observation 20752a50-7f8b-4fe1-9954-a4a0a4f71003 · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 10

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Observation f392d120-38cc-45d0-a112-cabaf9c85ffc · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 11

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Observation 37b3f060-aefc-4fb2-9213-92a13b1f6f0a · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Unetr: Transformers for 3d medical image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.363005Z

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Observation 6288241f-e745-4caf-8989-9d17c3c93526 · outbound

This paper cites A robust volumetric transformer for accurate 3d tumor segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images A robust volumetric transformer for accurate 3d tumor segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.348422Z

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Observation 6deee9fe-0a4e-4a50-af0a-ef6d6a2e0975 · outbound

This paper cites Levit-unet:Makefasterencoderswithtransformerformedicalimagesegmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Levit-unet:Makefasterencoderswithtransformerformedicalimagesegmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.334391Z

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Observation 23777f92-369e-46bc-a986-921844b1e557 · outbound

This paper cites STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 15

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Observation 1eac245f-cbdd-448a-b766-2a6751330bc8 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Scribble-based hierarchical weakly supervised learning for brain tumor segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.319662Z

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Observation 5578af26-0652-4e40-93fe-fdda05727e9e · outbound

This paper cites Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.305256Z

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Observation 09a0fb5b-3c33-4078-8424-7b6946febde2 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Scribblevc: Scribble-supervised medical image segmentation with vision-class embedding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.289900Z

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Observation ab08a517-9230-4e03-baa6-d286a3af8db8 · outbound

This paper cites Scribformer: Transformer makes cnn work better for scribble-based medical image segmentation.IEEE Transactions on Medical Imaging, 2024.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Scribformer: Transformer makes cnn work better for scribble-based medical image segmentation.IEEE Transactions on Medical Imaging, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.272600Z

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Observation 28843eac-64d5-407a-a96e-842a649ed4cd · outbound

This paper cites Weakly-supervisedsalientobjectdetectionviascribbleannotations.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Weakly-supervisedsalientobjectdetectionviascribbleannotations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.257056Z

Source-reported events for the cited work

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Observation 6021e589-abca-4a44-b605-40c764f320d8 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Cyclemix: A holistic strategy for medical image segmentation from scribble supervision

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.241335Z

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Observation 778662c4-27e8-42ab-903b-9892223acf08 · outbound

This paper cites S 2 me: Spatial-spectral mutual teaching and ensemble learning for scribble-supervised polyp segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images S 2 me: Spatial-spectral mutual teaching and ensemble learning for scribble-supervised polyp segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.226165Z

Source-reported events for the cited work

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Observation 2896beb1-1536-43c4-8deb-88fc4d827c17 · outbound

This paper cites In European conference on computer vision, pages 459–479.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images In European conference on computer vision, pages 459–479

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.211466Z

Source-reported events for the cited work

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Observation 5a65de7a-60ab-42b8-ade9-dbf51e0c9275 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018

Reference 24

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Observation bd1e7627-1085-490a-a298-5eb9c2d45a5d · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Learning to segment from scribbles using multi-scale adversarial attention gates

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.186162Z

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Observation 3e20839d-2181-4a6c-9edb-960065fde078 · outbound

This paper cites Multivariatemixturemodelforcardiacsegmentationfrommulti-sequencemri.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Multivariatemixturemodelforcardiacsegmentationfrommulti-sequencemri

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.168575Z

Source-reported events for the cited work

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Observation f0bd1101-d852-4bf2-8a8b-f3435a2e026f · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12):2933–2946, 2019.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Multivariate mixture model for myocardial segmentation combining multi-source images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12):2933–2946, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.153111Z

Source-reported events for the cited work

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Observation ec05e760-c3ff-4c2f-b38d-5d5c00aaa41d · outbound

This paper cites Shapepu: A new pu learning framework regularized by global consistency for scribble supervised cardiac segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Shapepu: A new pu learning framework regularized by global consistency for scribble supervised cardiac segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.137298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28989abd-d12f-4fd9-a3c9-2a9938e4705c · outbound

This paper cites Video polyp segmentation: A deep learning perspective.Machine Intelligence Research, 19(6):531–549, 2022.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Video polyp segmentation: A deep learning perspective.Machine Intelligence Research, 19(6):531–549, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.121909Z

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Observation cd3f3e63-bd98-49a1-84cc-a0757e54e283 · outbound

This paper cites Progressively normalized self-attention networkforvideopolypsegmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Progressively normalized self-attention networkforvideopolypsegmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.106138Z

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Observation 256cbe0d-f2b2-44b1-8d22-791e992ff3a6 · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Pranet: Parallel reverse attention network for polyp segmentation

Reference 31

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QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:41:46.081296Z

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Observation 00f11c44-67b5-43cd-804a-419ea4a1bde9 · outbound

This paper cites Dataset of breast ultrasound images.Data in brief, 28:104863, 2020.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Dataset of breast ultrasound images.Data in brief, 28:104863, 2020

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.066374Z

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Observation e1bbeb0d-e1e2-4fd0-ad76-921b7309c6d5 · outbound

This paper cites The treasure beneath multiple annotations: An uncertainty-aware edge detector.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images The treasure beneath multiple annotations: An uncertainty-aware edge detector

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.050869Z

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Observation a3799e22-67a3-44fd-a6f0-495657c62a9b · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Masked-attention mask transformer for universal image segmentation

Reference 35

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Observation fc36b10b-121d-401b-b554-1bccbee65542 · outbound

This paper cites Mask2former with improved query for semantic segmentation in remote-sensing images.Mathematics, 12(5):765, 2024.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Mask2former with improved query for semantic segmentation in remote-sensing images.Mathematics, 12(5):765, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.027176Z

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Observation b67a5e65-4a91-4e51-b7cf-55db0867c36e · outbound

This paper cites MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

Reference 37

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Observation 720f4b0e-524f-4e6c-a492-86052b66bb7d · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Imagenet: A large-scale hierarchical image database

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.567680Z

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Observation f4268945-50fe-4d0f-abe5-b34076aa9b88 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:46.002359Z

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Observation 9298f4ee-a233-4507-bd92-0ee1ccb31640 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Deep Learning using Rectified Linear Units (ReLU)

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.576208Z

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Observation baf5f6da-2cb4-4ebd-b718-be18db2e2228 · outbound

This paper cites Et-net: A generic edge-attention guidance network for medical image segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Et-net: A generic edge-attention guidance network for medical image segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.986253Z

Source-reported events for the cited work

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Observation 7652364f-1c55-4839-b500-371d4d4a7ca3 · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation.Advances in neural information processing systems, 34:17864–17875, 2021.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Per-pixel classification is not all you need for semantic segmentation.Advances in neural information processing systems, 34:17864–17875, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.969258Z

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Observation f2eea5cb-31c9-412f-b4f4-04adb38b4510 · outbound

This paper cites Query-guided generalizable medical image segmentation.Pattern Recognition Letters, 184:52–58, 2024.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Query-guided generalizable medical image segmentation.Pattern Recognition Letters, 184:52–58, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.954077Z

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Observation c448f7dc-182d-4b57-bd18-3cd85aece2b8 · outbound

This paper cites Segment anything.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Segment anything

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.938442Z

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Observation fceeb8eb-d49c-4d92-83da-3b075da5cb30 · outbound

This paper cites Sparse instance activation for real-time instance segmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Sparse instance activation for real-time instance segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.922930Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9dfcba5b-941e-4bf3-81fc-3a217e59c971 · outbound

This paper cites In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.908037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eaba26fe-900a-4dd9-86fb-66ee8d761d54 · outbound

This paper cites Pyramid scene parsing network.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Pyramid scene parsing network

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.609129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebeb5e54-11c5-4877-ae36-b53fdb493de6 · outbound

This paper cites Puzzle mix: Exploiting saliency and local statistics for optimal mixup.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Puzzle mix: Exploiting saliency and local statistics for optimal mixup

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.881521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ca5c142-c0b0-4c17-be2e-1475fb1edf1f · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Improved Regularization of Convolutional Neural Networks with Cutout

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.617372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88fd0921-83f1-49da-b17b-cc7a5dad3c49 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images mixup: Beyond Empirical Risk Minimization

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T18:41:45.621898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 517d6e1e-4067-49a4-adc4-dd50903e993c · outbound

This paper cites Scribblesup:Scribble-supervisedconvolutionalnetworksforsemanticsegmentation.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Scribblesup:Scribble-supervisedconvolutionalnetworksforsemanticsegmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.866596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation be001033-8db7-425d-88bf-a649dc13b696 · outbound

This paper cites Semi-supervisedlearningbyentropyminimization.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Semi-supervisedlearningbyentropyminimization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.851409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 92587bea-abe3-4ba5-bd40-972efaa498fb · outbound

This paper cites Weakly supervised segmentation of covid19 infection with scribble annotation on ct images.Pattern recognition, 122:108341, 2022.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Weakly supervised segmentation of covid19 infection with scribble annotation on ct images.Pattern recognition, 122:108341, 2022

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.836753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 62c35a76-0b78-4f7f-b298-c26955ed3f19 · outbound

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

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images Semi-supervised semantic segmentation with cross pseudo supervision

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:41:45.820619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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