Adaptive Learning for Medical Image Classification across Domains

In the rapidly advancing area of semi-supervised learning (SSL), especially concerning large-scale computer vision models and medical imaging, pseudo-labeling has become a central strategy. Nevertheless, this technique faces significant hurdles, including the possibility of integrating erroneously pseudo-labeled data due to improper threshold settings and the underutilization of data with low-confidence pseudo-labels. To overcome these challenges, we introduce a novel SSL framework for training large models, termed PE-DAT, which fuses Pseudo-loss Estimation with Domain-Adaptive Training. This integration substantially enhances the accuracy of medical image classification tasks, addressing both multiclass and multi-label problems. A pivotal aspect of our approach is a cutting-edge data selection method based on the concept that genuine data exhibit lower pseudo-loss values compared to their noisy counterparts. The PE-DAT framework’s effectiveness is demonstrated through its superior performance relative to existing benchmarks, validated by extensive testing on a wide array of medical and natural image datasets.