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To improve the model’s robustness and generalization performance, we investigate effective test-time augmentation-based ensemble prediction methods and evaluate the effectiveness of various ensemble prediction techniques in combination. In the training phase, we generate an optimized predictive model using a multi-modality regression network. The prediction is then determined through ensemble average voting with augmented test images generated by diverse data augmentation methods, including affine transformation, Mixup, Cutout, CutMix, and their combinations. Our experimentation reveal that all ensemble prediction methods demonstrated the ability to address issues through regularization, such as averaging errors on images subjected to random modifications. Notably, the use of Affine significantly improves over the baseline, with a 18.3% increase in accuracy and a 12.2% increase in AUC. The adoption of CutMix, maintains stability in both sensitivity and specificity, resulting in a higher balanced accuracy than Mixup and Cutout.
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