Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification
This work addresses the high cost of expert annotations in medical imaging by improving semi-supervised learning, though it appears incremental as it builds on existing SSL methods with specific structural enhancements.
The paper tackles the problem of limited labeled data in medical image classification by proposing a Spatio-Temporal Structure Consistent learning framework, which outperforms state-of-the-art semi-supervised methods on datasets like ISIC 2018 and ChestX-ray14.
Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal Structure Consistent (STSC) learning framework. Specifically, a gram matrix is derived to combine the spatial structure consistency and temporal structure consistency together. This gram matrix captures the structural similarity among the representations of different training samples. At the spatial level, our framework explicitly enforces the consistency of structural similarity among different samples under perturbations. At the temporal level, we consider the consistency of the structural similarity in different training iterations by digging out the stable sub-structures in a relation graph. Experiments on two medical image datasets (i.e., ISIC 2018 challenge and ChestX-ray14) show that our method outperforms state-of-the-art SSL methods. Furthermore, extensive qualitative analysis on the Gram matrices and heatmaps by Grad-CAM are presented to validate the effectiveness of our method.