Hybrid(Transformer+CNN)-based Polyp Segmentation | Paper Citation Record

Observation 34d536fc-7b75-4489-803a-fd2020631e8d · outbound

This paper cites Edd2020: A comprehensive dataset for en- doscopic artifact detection.

Hybrid(Transformer+CNN)-based Polyp Segmentation Edd2020: A comprehensive dataset for en- doscopic artifact detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.913989Z

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Observation 33bfda1f-280b-4946-bb55-3ff1bd2ddaec · outbound

This paper cites Polypgen: A multi-center polyp detection and segmentation dataset.

Hybrid(Transformer+CNN)-based Polyp Segmentation Polypgen: A multi-center polyp detection and segmentation dataset

Reference 2

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This paper cites an unresolved cited work.

Hybrid(Transformer+CNN)-based Polyp Segmentation Unresolved cited work

Reference 3

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Observation f24e5623-d2d9-4be7-a5a6-4adfe60f1510 · outbound

This paper cites Artificial intelligence in knee arthroplasty: Current concept of the available clinical applications.

Hybrid(Transformer+CNN)-based Polyp Segmentation Artificial intelligence in knee arthroplasty: Current concept of the available clinical applications

Reference 4

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Observation 97f5208b-0f8c-4d4e-bec1-c9230072521c · outbound

This paper cites To- wards automatic polyp detection with a polyp appearance model.

Hybrid(Transformer+CNN)-based Polyp Segmentation To- wards automatic polyp detection with a polyp appearance model

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.859142Z

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Observation 83e28350-b95b-4d74-809e-f127140194e7 · outbound

This paper cites Sanchez, G.

Hybrid(Transformer+CNN)-based Polyp Segmentation Sanchez, G

Reference 6

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Observation cca4c5e4-d21a-4e19-9a89-1228505ae325 · outbound

This paper cites Wm- dova maps for accurate polyp highlighting in colonoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Wm- dova maps for accurate polyp highlighting in colonoscopy

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.833438Z

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Observation b3e1cfa6-360b-44c8-9900-45024720cb8f · outbound

This paper cites Comparative validation of polyp detection methods in video colonoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Comparative validation of polyp detection methods in video colonoscopy

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.820364Z

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Observation ef93a6c7-1477-4eb1-95c0-5babdda7ec04 · outbound

This paper cites Un- derstanding robustness of transformers for image classifica- tion.

Hybrid(Transformer+CNN)-based Polyp Segmentation Un- derstanding robustness of transformers for image classifica- tion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.806957Z

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Observation e8740568-0b29-4a2c-9679-14cc67e41d8c · outbound

This paper cites Hyperkvasir: A comprehensive multi- class image and video dataset for gastrointestinal endoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Hyperkvasir: A comprehensive multi- class image and video dataset for gastrointestinal endoscopy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.794289Z

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Observation 7293c825-40aa-4328-aebf-be78a579e227 · outbound

This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T23:06:52.678144Z

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Observation 2d920f53-b2b3-4c4d-9509-633489d1d509 · outbound

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

Hybrid(Transformer+CNN)-based Polyp Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T23:06:52.682629Z

Source-reported events for the cited work

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Observation 925e43aa-ceef-4d85-a204-217e94512c2d · outbound

This paper cites Polyp-pvt: Polyp segmentation with pyramid vision transformers.

Hybrid(Transformer+CNN)-based Polyp Segmentation Polyp-pvt: Polyp segmentation with pyramid vision transformers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.781463Z

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Observation be3388cd-1fa7-4b98-8085-aef3e324459e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Hybrid(Transformer+CNN)-based Polyp Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.768274Z

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Observation d457a9aa-0bd4-457a-95b3-96d0667d2d1e · outbound

This paper cites Espinosa, Gaston A.

Hybrid(Transformer+CNN)-based Polyp Segmentation Espinosa, Gaston A

Reference 15

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Observation 4c8f9513-d068-4af1-9127-8c0317561123 · outbound

This paper cites Sun-seg: A large-scale dataset for sur- gical scene segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Sun-seg: A large-scale dataset for sur- gical scene segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.742168Z

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Observation 41edbae3-eed5-4a21-a802-37f493efc4c2 · outbound

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

Hybrid(Transformer+CNN)-based Polyp Segmentation Pranet: Parallel reverse attention network for polyp segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.729107Z

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Observation c96f3b20-24bd-4137-a438-f996007c9eee · outbound

This paper cites Roth, and Daguang Xu.

Hybrid(Transformer+CNN)-based Polyp Segmentation Roth, and Daguang Xu

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.715820Z

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Observation b8ba2dfc-c339-49c6-8193-f8cefec94ece · outbound

This paper cites Deep residual learning for image recognition.

Hybrid(Transformer+CNN)-based Polyp Segmentation Deep residual learning for image recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.702697Z

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Observation 1a93f96f-d705-48a3-a0f4-59aad595b1d9 · outbound

This paper cites Kvasir-capsule: A video capsule en- doscopy dataset.

Hybrid(Transformer+CNN)-based Polyp Segmentation Kvasir-capsule: A video capsule en- doscopy dataset

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.689558Z

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Observation 3dd98453-80a0-40a8-9881-224ba08c2c1a · outbound

This paper cites Piccolo dataset for endoscopic polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Piccolo dataset for endoscopic polyp segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.676870Z

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Observation 98d4faab-db09-48ee-b4f1-9e3d1c0ae559 · outbound

This paper cites Real- time polyp detection, localization and segmentation in colonoscopy using deep learning.

Hybrid(Transformer+CNN)-based Polyp Segmentation Real- time polyp detection, localization and segmentation in colonoscopy using deep learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.663330Z

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Observation 947e05fc-cc86-4f32-acb4-5c8cf143fef1 · outbound

This paper cites Johansen, Dag Johansen, Jens Rittscher, Michael A.

Hybrid(Transformer+CNN)-based Polyp Segmentation Johansen, Dag Johansen, Jens Rittscher, Michael A

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.650607Z

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Observation cc7feaae-009d-46e7-96c3-2203f84ea41d · outbound

This paper cites Smedsrud, Daniel Johansen, et al.

Hybrid(Transformer+CNN)-based Polyp Segmentation Smedsrud, Daniel Johansen, et al

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.637689Z

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This paper cites Smedsrud, Michael A.

Hybrid(Transformer+CNN)-based Polyp Segmentation Smedsrud, Michael A

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.624411Z

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Observation fe26f35c-167c-4619-9884-039924d94ec8 · outbound

This paper cites Kvasir-SEG: A Segmented Polyp Dataset.

Hybrid(Transformer+CNN)-based Polyp Segmentation Kvasir-SEG: A Segmented Polyp Dataset

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:06:53.016997Z

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Observation 246ff0e1-5017-4c4b-a0fd-0a4534bfd88c · outbound

This paper cites Smedsrud, Michael A.

Hybrid(Transformer+CNN)-based Polyp Segmentation Smedsrud, Michael A

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.611824Z

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Observation d89e7c9b-5ac5-4988-a325-2b01229f3aa8 · outbound

This paper cites Nanonet: real-time polyp segmentation in video capsule endoscopy and colonoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Nanonet: real-time polyp segmentation in video capsule endoscopy and colonoscopy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.599104Z

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Observation afc5622e-46d5-4079-aa58-4fd030b804b9 · outbound

This paper cites Deep learning-based detection of polyps in colonoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Deep learning-based detection of polyps in colonoscopy

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.586117Z

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Observation 8eaf62aa-afd9-466d-a74b-bbb2fd9a56df · outbound

This paper cites Li-segpnet: A lightweight pyramid network for real-time polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Li-segpnet: A lightweight pyramid network for real-time polyp segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.573307Z

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Observation 4a9ce29b-efba-4cd3-bc26-f71d76b253e9 · outbound

This paper cites Sun: A large-scale dataset for surgical understanding.

Hybrid(Transformer+CNN)-based Polyp Segmentation Sun: A large-scale dataset for surgical understanding

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.559756Z

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Observation 3358be66-4f28-4981-b808-158613ca0334 · outbound

This paper cites Cp-child: A pediatric colon polyp dataset.

Hybrid(Transformer+CNN)-based Polyp Segmentation Cp-child: A pediatric colon polyp dataset

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.547032Z

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Observation 698c84be-6a96-40cc-a9dd-d153373667b9 · outbound

This paper cites Ddanet: Dual decoder attention network for automatic polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Ddanet: Dual decoder attention network for automatic polyp segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.534086Z

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Observation 590d5724-ec4b-483d-80b1-043df92aa056 · outbound

This paper cites Ld polyp video: A large-scale colonoscopy video dataset.

Hybrid(Transformer+CNN)-based Polyp Segmentation Ld polyp video: A large-scale colonoscopy video dataset

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.521221Z

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Observation 7c07922a-eaf3-462d-9e88-d84f43151f72 · outbound

This paper cites Swin trans- former: Hierarchical vision transformer using shifted win- dows.

Hybrid(Transformer+CNN)-based Polyp Segmentation Swin trans- former: Hierarchical vision transformer using shifted win- dows

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.507942Z

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Observation 59406eaa-bfab-49e7-a750-1a520db560db · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Fully convolutional networks for semantic segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.494289Z

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Observation f94b10e4-2234-45c3-ab79-807d10316b1d · outbound

This paper cites Decoupled weight de- cay regularization.

Hybrid(Transformer+CNN)-based Polyp Segmentation Decoupled weight de- cay regularization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.481458Z

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Observation 42722c43-806d-4dac-9912-f9c0562788a8 · outbound

This paper cites Mamonov, Isabel N.

Hybrid(Transformer+CNN)-based Polyp Segmentation Mamonov, Isabel N

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.468555Z

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Observation d4d141cb-b87e-47ac-98cd-e79b006d1ee1 · outbound

This paper cites Matsuda, A.

Hybrid(Transformer+CNN)-based Polyp Segmentation Matsuda, A

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.454855Z

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Observation d0c096fc-14a7-49bf-9b13-23f813ddb8f4 · outbound

This paper cites Understanding your diagno- sis: Colonoscopy.

Hybrid(Transformer+CNN)-based Polyp Segmentation Understanding your diagno- sis: Colonoscopy

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.441851Z

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Observation d0fa745c-0dbc-4169-973e-baf42541a97d · outbound

This paper cites A deep learning method for early detection of dia- betic foot using decision fusion and thermal images.

Hybrid(Transformer+CNN)-based Polyp Segmentation A deep learning method for early detection of dia- betic foot using decision fusion and thermal images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.428470Z

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Observation 6d219cb7-1837-4fd1-acee-c1bb238904d1 · outbound

This paper cites Cancer stat facts: Colorectal cancer.

Hybrid(Transformer+CNN)-based Polyp Segmentation Cancer stat facts: Colorectal cancer

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.415486Z

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Observation 7dd932a5-8ebe-4206-80ab-6a94ff29e9c5 · outbound

This paper cites Uacanet: Unified adaptive context-aware network for polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Uacanet: Unified adaptive context-aware network for polyp segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.401270Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 570a2294-446a-4188-967a-6eeae6728253 · outbound

This paper cites Deep learning and hand-crafted fea- tures for automatic polyp detection.

Hybrid(Transformer+CNN)-based Polyp Segmentation Deep learning and hand-crafted fea- tures for automatic polyp detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.387996Z

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Observation 42475eb4-ae1f-4424-870c-a651718fe673 · outbound

This paper cites Nbi and wl ucdb databases for computer-assisted detection of ulcerative lesions.

Hybrid(Transformer+CNN)-based Polyp Segmentation Nbi and wl ucdb databases for computer-assisted detection of ulcerative lesions

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.374261Z

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Observation 13d6ecc6-5e22-4c8e-9908-712ef1cfa5a6 · outbound

This paper cites Rodr ´ıguez-Merch´an and Pilar G ´omez-Cardero.

Hybrid(Transformer+CNN)-based Polyp Segmentation Rodr ´ıguez-Merch´an and Pilar G ´omez-Cardero

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.361532Z

Source-reported events for the cited work

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Observation 0299050e-ba7d-43fb-a0bf-5f29a9c6c4c0 · outbound

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

Hybrid(Transformer+CNN)-based Polyp Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.348576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c1774b4-4774-4172-b6c2-0e9244d3b0fe · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer.

Hybrid(Transformer+CNN)-based Polyp Segmentation Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.335929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e8c93cf-2197-4d31-a486-fc3c6e73e6d8 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer.

Hybrid(Transformer+CNN)-based Polyp Segmentation Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.322718Z

Source-reported events for the cited work

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Observation 7f35dbce-3d7f-41f0-b501-59cbb8765bc6 · outbound

This paper cites Very deep con- volutional networks for large-scale image recognition.

Hybrid(Transformer+CNN)-based Polyp Segmentation Very deep con- volutional networks for large-scale image recognition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.310369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b73f2cd1-6ff8-4f4f-81e6-6a6bf934e71a · outbound

This paper cites an unresolved cited work.

Hybrid(Transformer+CNN)-based Polyp Segmentation Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-05T23:06:53.297786Z

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Observation fb76f631-00bf-43d0-9252-f0bcd55e2226 · outbound

This paper cites The effect of a graft transformation on distance signless Laplacian spectral radius of the graphs.

Hybrid(Transformer+CNN)-based Polyp Segmentation The effect of a graft transformation on distance signless Laplacian spectral radius of the graphs

Reference 52

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T23:06:52.981849Z

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Observation e7ebed61-f952-45eb-87f3-2fc8f85b320c · outbound

This paper cites Gurudu, and Jianming Liang.

Hybrid(Transformer+CNN)-based Polyp Segmentation Gurudu, and Jianming Liang

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.285044Z

Source-reported events for the cited work

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This paper cites Automated polyp detection in colonoscopy videos using shape and context information.

Hybrid(Transformer+CNN)-based Polyp Segmentation Automated polyp detection in colonoscopy videos using shape and context information

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.272386Z

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Hybrid(Transformer+CNN)-based Polyp Segmentation Unresolved cited work

Reference 55

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This paper cites Atten- tion is all you need.

Hybrid(Transformer+CNN)-based Polyp Segmentation Atten- tion is all you need

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.246320Z

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This paper cites Sanchez, et al.

Hybrid(Transformer+CNN)-based Polyp Segmentation Sanchez, et al

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.232814Z

Source-reported events for the cited work

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This paper cites Vezakis, Konstantinos Georgas, Dimitrios Fo- tiadis, and George K.

Hybrid(Transformer+CNN)-based Polyp Segmentation Vezakis, Konstantinos Georgas, Dimitrios Fo- tiadis, and George K

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.219501Z

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This paper cites Duck-net: Dense u-shaped convolutional ker- nel network for polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Duck-net: Dense u-shaped convolutional ker- nel network for polyp segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.206062Z

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This paper cites Nanonet: Real-time polyp segmentation with ultra-lightweight models for edge devices.

Hybrid(Transformer+CNN)-based Polyp Segmentation Nanonet: Real-time polyp segmentation with ultra-lightweight models for edge devices

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.192501Z

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Observation 27569c0f-08c3-43c5-a387-c78ab97213b8 · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

Hybrid(Transformer+CNN)-based Polyp Segmentation Pvt v2: Improved baselines with pyramid vision transformer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.178342Z

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Observation cac81e29-bf76-48df-96dc-9f0402898863 · outbound

This paper cites A versatile back- bone for dense prediction without convolutions.

Hybrid(Transformer+CNN)-based Polyp Segmentation A versatile back- bone for dense prediction without convolutions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.164604Z

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Observation 0f71ea7e-c71d-42ad-9783-442bbc11e3d3 · outbound

This paper cites Msrfe-net: Multi-scale residual feature enhancement network for polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Msrfe-net: Multi-scale residual feature enhancement network for polyp segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.150840Z

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This paper cites Williams, Domenico Borroni, Renqiang Liu, et al.

Hybrid(Transformer+CNN)-based Polyp Segmentation Williams, Domenico Borroni, Renqiang Liu, et al

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.136471Z

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Observation da9526a6-dd0d-4673-8af0-921ec0fb2274 · outbound

This paper cites Alvarez, and Ping Luo.

Hybrid(Transformer+CNN)-based Polyp Segmentation Alvarez, and Ping Luo

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.121044Z

Source-reported events for the cited work

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Observation 36b5bfa6-82f6-4bf8-8e0d-afb56fa9bb6b · outbound

This paper cites Colonformer: An efficient transformer based method for colon polyp segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Colonformer: An efficient transformer based method for colon polyp segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.107346Z

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Observation ea3f4e80-74ce-4834-a3d8-f776b17708f1 · outbound

This paper cites Fcb- former: A fully convolutional bridge transformer with pyra- mid squeeze-excitation for colonoscopy segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation Fcb- former: A fully convolutional bridge transformer with pyra- mid squeeze-excitation for colonoscopy segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.093385Z

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Observation 8c628e4d-4203-4d94-8922-c8d4268859c3 · outbound

This paper cites Road extraction by deep residual u-net.

Hybrid(Transformer+CNN)-based Polyp Segmentation Road extraction by deep residual u-net

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.078815Z

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Observation feb87228-fc65-4767-a4dc-34648f5d4af4 · outbound

This paper cites Road ex- traction by deep residual u-net.

Hybrid(Transformer+CNN)-based Polyp Segmentation Road ex- traction by deep residual u-net

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:06:53.063941Z

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Observation d1a30977-27c5-4cf3-8fe8-362636db652a · outbound

This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation.

Hybrid(Transformer+CNN)-based Polyp Segmentation UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Reference 70

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Hybrid(Transformer+CNN)-based Polyp Segmentation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2015

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Source-reported events for the cited work

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