UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets
This addresses the problem of limited data and unknown classes in medical imaging for improved computer-aided diagnosis, though it appears incremental as it builds on existing simplex-based methods.
The paper tackled open-set recognition in medical image diagnosis by proposing an uncertainty-aware deep simplex classifier that effectively rejects unknown class samples, achieving significant performance gains across multiple MedMNIST datasets and a public skin dataset, outperforming state-of-the-art methods.
Driven by advancements in deep learning, computer-aided diagnoses have made remarkable progress. However, outside controlled laboratory settings, algorithms may encounter several challenges. In the medical domain, these difficulties often stem from limited data availability due to ethical and legal restrictions, as well as the high cost and time required for expert annotations-especially in the face of emerging or rare diseases. In this context, open-set recognition plays a vital role by identifying whether a sample belongs to one of the known classes seen during training or should be rejected as an unknown. Recent studies have shown that features learned in the later stages of deep neural networks are observed to cluster around their class means, which themselves are arranged as individual vertices of a regular simplex [32]. The proposed method introduces a loss function designed to reject samples of unknown classes effectively by penalizing open space regions using auxiliary datasets. This approach achieves significant performance gain across four MedMNIST datasets-BloodMNIST, OCTMNIST, DermaMNIST, TissueMNIST and a publicly available skin dataset [29] outperforming state-of-the-art techniques.