S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation | Paper Citation Record

Observation aa642323-9ca9-4b4b-8aef-422c82b52bf9 · outbound

This paper cites Lung cancer prediction using electronic claims records: A transformer-based approach,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Lung cancer prediction using electronic claims records: A transformer-based approach,

Reference 1

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Observation 0184a7ae-ef8a-42ba-9a58-80438c08710e · outbound

This paper cites Modality- specific segmentation network for lung tumor segmenta- tion in pet-ct images,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Modality- specific segmentation network for lung tumor segmenta- tion in pet-ct images,

Reference 2

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Observation bd4dbc19-cd32-4fb2-a30e-1632935eeb86 · outbound

This paper cites Deep learning methods for lung cancer segmentation in whole-slide histopathology images—the acdc@ lunghp challenge 2019,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Deep learning methods for lung cancer segmentation in whole-slide histopathology images—the acdc@ lunghp challenge 2019,

Reference 3

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Observation ae2f82cd-3c8c-4b67-8be4-6ec1576bbb3a · outbound

This paper cites Spectrum of lung adenocarcinoma,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Spectrum of lung adenocarcinoma,

Reference 4

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This paper cites Lung adenocarcinoma,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Lung adenocarcinoma,

Reference 5

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Observation ffb97f6d-d356-485d-b82f-7a91af01da4c · outbound

This paper cites Prognostic considerations of the new world health organization classification of lung adeno- carcinoma,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Prognostic considerations of the new world health organization classification of lung adeno- carcinoma,

Reference 6

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This paper cites Who classification of tumours of the lung, pleura, thymus and heart,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Who classification of tumours of the lung, pleura, thymus and heart,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:36.126008Z

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Observation 10f48ed2-f395-4962-b5a4-a6278a336f2a · outbound

This paper cites Role of pet/ct in management of early lung adenocarcinoma,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Role of pet/ct in management of early lung adenocarcinoma,

Reference 8

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Observation cf0d075c-8df4-45b3-bd7f-4ccb74587ae8 · outbound

This paper cites Lung ade- nocarcinomas: correlation of computed tomography and pathology findings,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Lung ade- nocarcinomas: correlation of computed tomography and pathology findings,

Reference 9

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Observation 75dba093-127b-42e4-b3d1-f52ea8509e47 · outbound

This paper cites Integrating lung parenchyma segmentation and nodule detection with deep multi-task learning,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Integrating lung parenchyma segmentation and nodule detection with deep multi-task learning,

Reference 10

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Observation 95315021-66b2-4eb5-92a1-6c616a9780bc · outbound

This paper cites Advancing pose-guided image synthesis with progressive conditional diffusion models,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Advancing pose-guided image synthesis with progressive conditional diffusion models,

Reference 11

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Observation b3898e88-10dd-431e-ac94-5ed17c741cd0 · outbound

This paper cites Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models

Reference 12

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Observation a55caa7a-050e-4dd4-bfac-2132552d8f97 · outbound

This paper cites Pulmonary nodule detection in medical images: a survey,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Pulmonary nodule detection in medical images: a survey,

Reference 13

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Observation ec254fc5-9c3b-4fee-85e2-7448b3ea72bc · outbound

This paper cites Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma,

Reference 14

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This paper cites A bag of tricks for fine-grained roof extraction,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A bag of tricks for fine-grained roof extraction,

Reference 15

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Observation 59ff64bc-7bb6-4483-801b-414832ad56f7 · outbound

This paper cites A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive fcm algorithm,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive fcm algorithm,

Reference 16

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Observation 00b3f60c-4d3e-410b-b94b-4152db9ceb68 · outbound

This paper cites Au- tomated pulmonary nodule detection in ct images using deep convolutional neural networks,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Au- tomated pulmonary nodule detection in ct images using deep convolutional neural networks,

Reference 17

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Observation 36fe5087-c9a5-4308-b909-d960de83f049 · outbound

This paper cites Pulmonary lung nodule detection from computed tomography images using two-stage convolutional neural network,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Pulmonary lung nodule detection from computed tomography images using two-stage convolutional neural network,

Reference 18

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Observation 356125d7-b23e-4387-993e-88117356f5de · outbound

This paper cites A rubust method for roof extraction and height estimation,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A rubust method for roof extraction and height estimation,

Reference 19

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This paper cites Segmentation of the pulmonary nodule and the attached vessels in the ct scan of the chest using morphological features and topological skeleton of the nodule,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Segmentation of the pulmonary nodule and the attached vessels in the ct scan of the chest using morphological features and topological skeleton of the nodule,

Reference 20

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This paper cites Clustering by transmission learning from data density to label manifold with statistical diffusion,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Clustering by transmission learning from data density to label manifold with statistical diffusion,

Reference 21

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Observation 4cbef3ef-9ea8-4e65-b605-c8710b26d1f4 · outbound

This paper cites U-net: Con- volutional networks for biomedical image segmenta- tion,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation U-net: Con- volutional networks for biomedical image segmenta- tion,

Reference 22

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Observation 1e7ac419-c7df-4f15-97c5-40e053719b59 · outbound

This paper cites Review of semantic segmentation of medical images using mod- ified architectures of unet,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Review of semantic segmentation of medical images using mod- ified architectures of unet,

Reference 23

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Observation 8f1248d7-2976-43f6-8dbb-14fe2675c90c · outbound

This paper cites A cascaded dual-pathway residual network for lung nodule segmentation in ct images,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A cascaded dual-pathway residual network for lung nodule segmentation in ct images,

Reference 24

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Observation 10c5ce6a-06cd-4960-a4e9-23b52b6c8808 · outbound

This paper cites A novel deep learning network and its application for pulmonary nodule segmentation,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A novel deep learning network and its application for pulmonary nodule segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:35.940795Z

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This paper cites Pulmonary nodules segmentation based on crf 3d-unet structure,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Pulmonary nodules segmentation based on crf 3d-unet structure,

Reference 26

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This paper cites Vits vs. cnns for 3d medical image segmentation: Are transformers all you need?.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Vits vs. cnns for 3d medical image segmentation: Are transformers all you need?

Reference 27

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Observation 6b93c83c-8640-4273-9732-cf0d4e9fa6ea · outbound

This paper cites Attention is all you need,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Attention is all you need,

Reference 28

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This paper cites Enhancing landslide segmen- tation with guide attention mechanism and fast fourier transformer,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Enhancing landslide segmen- tation with guide attention mechanism and fast fourier transformer,

Reference 29

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Observation 52fd3da0-b1b3-4faa-b0dc-56dbef655e53 · outbound

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

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 30

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This paper cites Swin transformer: Hierarchical vi- sion transformer using shifted windows,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Swin transformer: Hierarchical vi- sion transformer using shifted windows,

Reference 31

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This paper cites Dropout: a simple way to pre- vent neural networks from overfitting,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Dropout: a simple way to pre- vent neural networks from overfitting,

Reference 32

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S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Dropblock: A regu- larization method for convolutional networks,

Reference 33

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S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Visual attention network,

Reference 34

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Observation 03ab2c90-3f9f-4cf6-95ae-c17f0ad991e9 · outbound

This paper cites Freeu: Free lunch in diffusion u-net,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Freeu: Free lunch in diffusion u-net,

Reference 35

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S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Squeeze-and-excitation networks,

Reference 36

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This paper cites Malunet: A multi-attention and light-weight unet for skin lesion segmentation,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Malunet: A multi-attention and light-weight unet for skin lesion segmentation,

Reference 37

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This paper cites S2-mlp: Spatial-shift mlp architecture for vision,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation S2-mlp: Spatial-shift mlp architecture for vision,

Reference 38

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Observation 52258209-36cf-49dd-bad5-444f38986849 · outbound

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S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Mlp-mixer: An all-mlp architecture for vision,

Reference 39

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S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Multilayer perceptron (mlp),

Reference 40

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This paper cites Resnest: Split-attention networks,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Resnest: Split-attention networks,

Reference 41

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This paper cites Y-net: joint segmentation and classifi- cation for diagnosis of breast biopsy images,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Y-net: joint segmentation and classifi- cation for diagnosis of breast biopsy images,

Reference 42

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Observation 559a0f18-975c-4cb7-8a08-dfb60c46f02f · outbound

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

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 43

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Observation ebd932cc-d2b9-4844-bcdc-f7f0e3ac0989 · outbound

This paper cites Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Reference 44

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This paper cites A bag of tricks for fine-grained roof extraction,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation A bag of tricks for fine-grained roof extraction,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:35.661773Z

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Observation c120ff76-5e6d-497d-9d66-42794ed1f375 · outbound

This paper cites Unet 3+: A full- scale connected unet for medical image segmentation,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Unet 3+: A full- scale connected unet for medical image segmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:35.651445Z

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Observation e0d54c1e-6024-48fa-b345-fe95cd318464 · outbound

This paper cites Sa-unet: Spatial attention u-net for retinal vessel segmentation,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Sa-unet: Spatial attention u-net for retinal vessel segmentation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:35.639899Z

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Observation bc3b5fdd-8ed9-483e-a42b-a40114998cbd · outbound

This paper cites Uncertainty- guided lung nodule segmentation with feature-aware attention,.

S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation Uncertainty- guided lung nodule segmentation with feature-aware attention,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:29:35.627802Z

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