Table 7.

Differences in deep learning models

RK	Model frameworks	Loss function	Training strategies
Sub-challenge 1: DR grading
1	EfficientNet33	SL1	MMoE + GMP + ES + OHEM + CV + O + T
2	EfficientNet33	SL1 + CE + DV + PL	CV + TTA
3	EfficientNet33	L1 + CE(5 class)	PLT
Sub-challenge 2: image quality assessment
1	SE-ResNeXt32	CE	TL
2	ResNet31	CS + L1	TL
3	VGG,30 UNet39	CE	TL
Sub-challenge 3: DR grading based on UWF fundus
1	EfficientNet33	L1 + CE(5 class)	PLT
2	EfficientNet33	SL1	MMoE + GMP + ES + OHEM + CV + O + T
3	EfficientNet33	CE	TL

SL1, smooth L1 loss; CE, cross-entropy loss; DV, dual view loss; PL, patient-level loss; CS, cost-sensitive loss;⁴⁰ L1, L1 loss; CE(5 class), mean loss of 5 class (one versus others); MMoE, multi-gate mixture of expert;⁴¹ GMP, generalized mean pooling;⁴² OHEM, online hard example mining;^43,44 CV, cross-validation; O, oversampling; ES, early stopping; TL, transfer learning; TTA, test time augmentation;^45,46 PLT, pseudo-labeled and labeled training.