SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention | Paper Citation Record

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This paper cites Addressing social determinants of vision health.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Addressing social determinants of vision health

Reference 1

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This paper cites Disparities in vision health and eye care.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Disparities in vision health and eye care

Reference 2

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This paper cites Artificial intelligence and deep learning in ophthalmology.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Artificial intelligence and deep learning in ophthalmology

Reference 3

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Observation 478281a0-9e2f-4f57-8816-a3e971d31f18 · outbound

This paper cites Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

Reference 4

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This paper cites A Comprehensive Survey on Detection of Ocular and Non-Ocular Diseases Using Color Fundus Images.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A Comprehensive Survey on Detection of Ocular and Non-Ocular Diseases Using Color Fundus Images

Reference 5

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This paper cites Advances in medical image analysis with vision transformers: a comprehensive review.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Advances in medical image analysis with vision transformers: a comprehensive review

Reference 6

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This paper cites Automated identification and grading system of diabetic retinopathy using deep neural networks.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Automated identification and grading system of diabetic retinopathy using deep neural networks

Reference 7

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Observation f8c4f50a-e975-4f41-8773-7c41d0c71d49 · outbound

This paper cites Machine Learning Approaches in High Myopia: Systematic Review and Meta- Analysis.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Machine Learning Approaches in High Myopia: Systematic Review and Meta- Analysis

Reference 8

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This paper cites Transformers in medical imaging: A survey.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Transformers in medical imaging: A survey

Reference 9

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This paper cites Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review

Reference 10

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Observation 3c311ac1-e8d0-42cb-92b5-e407377d3c12 · outbound

This paper cites A Review of CNN and Transformer Applications in Image Process- ing.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A Review of CNN and Transformer Applications in Image Process- ing

Reference 11

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This paper cites Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system

Reference 12

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This paper cites Lesion identification in fundus images via convolutional neural network-vision transformer.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Lesion identification in fundus images via convolutional neural network-vision transformer

Reference 13

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This paper cites Automatic feature learning to grade nuclear cataracts based on deep learning.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Automatic feature learning to grade nuclear cataracts based on deep learning

Reference 14

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This paper cites Diagnosis of diabetic retinopathy using deep neural networks.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Diagnosis of diabetic retinopathy using deep neural networks

Reference 15

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Observation 990087fa-2ceb-4c68-a9ab-37904f3fac4b · outbound

This paper cites Deep learning-based fully automated grading system for dry eye disease severity.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Deep learning-based fully automated grading system for dry eye disease severity

Reference 16

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Observation 7681971c-d2b9-41d1-9acd-b2cf475661a3 · outbound

This paper cites DIA-VXNET: A framework for automated diabetic eye disease detection using transfer learning with feature fusion network.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention DIA-VXNET: A framework for automated diabetic eye disease detection using transfer learning with feature fusion network

Reference 17

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Observation b8d5cade-d599-4be0-a75f-f9fd14368acd · outbound

This paper cites DeepDiabetic: an identification system of diabetic eye diseases using deep neural networks.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention DeepDiabetic: an identification system of diabetic eye diseases using deep neural networks

Reference 18

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Observation 96a564cc-aec9-4bfa-8b41-548dc38d7ebd · outbound

This paper cites A Novel Multi-Modal Deep Learning Framework for Early Detection of Ocular Diseases.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A Novel Multi-Modal Deep Learning Framework for Early Detection of Ocular Diseases

Reference 19

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This paper cites Challenges for ocular disease identifica- tion in the era of artificial intelligence.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Challenges for ocular disease identifica- tion in the era of artificial intelligence

Reference 20

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This paper cites Review of eye dis- eases detection and classification using deep learning techniques.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Review of eye dis- eases detection and classification using deep learning techniques

Reference 21

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Observation d38fcc0f-3c02-47f0-a02e-1cb07705be2d · outbound

This paper cites Classification of eye diseases in fundus images.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Classification of eye diseases in fundus images

Reference 22

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Swin transformer: Hierarchical vision transformer using shifted windows

Reference 23

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention ECA-Net: Efficient channel attention for deep convolutional neural net- works

Reference 24

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention The advance of deep learning and attention mechanism

Reference 25

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

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Convit: Improving vision transformers with soft convolutional inductive biases

Reference 27

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention BEiT: BERT Pre-Training of Image Transformers

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Maxvit: Multi-axis vision transformer

Reference 29

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Deep residual learning for image recognition

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This paper cites A dataset of color fundus images for the detection and classification of eye diseases.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A dataset of color fundus images for the detection and classification of eye diseases

Reference 31

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Unresolved cited work

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This paper cites The Age-Related Eye Disease Study (AREDS) system for classifying cataracts from photographs: AREDS report no. 4.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention The Age-Related Eye Disease Study (AREDS) system for classifying cataracts from photographs: AREDS report no. 4

Reference 33

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention DREAM: diabetic retinopa- thy analysis using machine learning

Reference 34

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Imagenet classification with deep convolutional neural networks

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A survey on deep learning in medical image analysis

Reference 36

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A review on medical image applications based on deep learning techniques

Reference 37

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This paper cites Analysis of deep learning techniques for prediction of eye diseases: A systematic review.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Analysis of deep learning techniques for prediction of eye diseases: A systematic review

Reference 38

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A study of CNN and transfer learning in medical imaging: Advan- tages, challenges, future scope

Reference 39

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Review of deep learning: concepts, CNN architectures, challenges, appli- cations, future directions

Reference 40

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention The application of artificial intelligence in glaucoma diagnosis and predic- tion

Reference 41

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Artificial intelligence in myopia: current and future trends

Reference 42

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Attention is all you need

Reference 43

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This paper cites Multi-label classification of retinal disease via a novel vision transformer model.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Multi-label classification of retinal disease via a novel vision transformer model

Reference 44

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Retinitis pigmentosa

Reference 45

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Pathology and pathogenesis of retinal detachment

Reference 46

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SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention “Myopia”

Reference 47

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This paper cites Gene and cell therapy for age-related macular degeneration: A review.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Gene and cell therapy for age-related macular degeneration: A review

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.459147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8e36a07d-99ad-4216-ab69-9ae2fbf1caf1 · outbound

This paper cites A review on glaucoma: causes, symptoms, pathogenesis & treatment.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A review on glaucoma: causes, symptoms, pathogenesis & treatment

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.447393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e355354-1473-41f3-a547-5b6ae03c900d · outbound

This paper cites Optic disc edema.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Optic disc edema

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.433632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80d9c4a1-0c53-485f-813a-0e1c3caee2dc · outbound

This paper cites A review on diabetic retinopathy.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention A review on diabetic retinopathy

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.422006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 243cc7be-234b-42a8-8284-433383f8ab8a · outbound

This paper cites Central serous chorioretinopathy: a review.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Central serous chorioretinopathy: a review

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.410268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d44d5b2d-6ca9-46a2-b75a-46e7c8708a6f · outbound

This paper cites Feature pyramid networks for object detection.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Feature pyramid networks for object detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.395451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 25f6cae9-fcc2-43b4-aa5b-b7f502a556fe · outbound

This paper cites Squeeze-and-excitation networks.

SwinECAT: A Transformer-based fundus disease classification model with Shifted Window Attention and Efficient Channel Attention Squeeze-and-excitation networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:17:13.382125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Reference resolution

Outbound references

Pith citing papers