UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets

The introduction of the UCDSC marks a significant advancement in the field of medical image analysis, driven by the rapid progress in deep learning technologies. Traditional algorithms often struggle outside controlled environments due to limited data and the complexities of expert annotation, particularly for rare diseases. The UCDSC addresses these challenges through open-set recognition, which is crucial for determining whether a sample belongs to known classes or should be classified as unknown. By implementing a specialized loss function that penalizes open space regions using auxiliary datasets, the UCDSC has shown remarkable performance improvements across four MedMNIST datasets: BloodMNIST, OCTMNIST, DermaMNIST, and TissueMNIST. This approach not only enhances diagnostic accuracy but also positions the UCDSC as a leading method in the realm of computer-aided diagnoses, outperforming existing state-of-the-art techniques.