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Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the sclera contains distinct personal features.
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis
Medical image classification plays a crucial role in computer-aided clinical diagnosis.
Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources
This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch.
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields.
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images
The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset.
