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Deep Multi-Task Learning for Diabetic Retinopathy Grading in Fundus Images

AAAI 2021 · Image generation › Super-resolution · approach: Real-world super-resolution · 49 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DeepMT-DR instead of the strongest listed baselineDR grading · DDRaccuracy: 79.5 -> 82.5Helped
Use DeepMT-DR instead of the strongest listed baselineDR grading · EyePACSaccuracy: 83.1 -> 85.7Helped
Jointly train the ISR, lesion segmentation, and grading tasks rather than remove ISR or train it independently (ablation)DeepMT-DR · DDRDR grading performanceHelped
Jointly train lesion segmentation with the other tasks rather than remove it or train it independently (ablation)DeepMT-DR · DDRDR grading performanceHelped
Add segmentation-aware loss (ablation)DeepMT-DR · DDRDR grading performanceHelped
Add classification-aware loss (ablation)DeepMT-DR · DDRDR grading performanceHelped
Use GMSV instead of GBP for network visualization (ablation)DeepMT-DR · DDRaccuracy: 81.4 -> 82.5Helped
Use GMSV instead of Grad-CAM for network visualization (ablation)DeepMT-DR · DDRaccuracy: 81.5 -> 82.5Helped
Use GMSV instead of SmoothBP for network visualization (ablation)DeepMT-DR · DDRaccuracy: 81.7 -> 82.5Helped
Use DeepMT-DR rather than MDRN for super-resolutionISR · DDRPSNR: 38.9 -> 39.7Helped
Use DeepMT-DR rather than MDRN for super-resolutionISR · DDRSSIM: 0.856 -> 0.883Helped

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