SkillNet: Open-Style Skill Acquisition and Adaptive Inference for Robust Biomedical Deep Learning

Deep neural networks are increasingly deployed in biomedical and healthcare applications, where robustness, adaptive knowledge reuse, and reliable prediction on rare or difficult cases are critical. However, biomedical datasets often evolve over time and contain rare patterns that can be easily forgotten during model updates. As a result, current DNN model building suffers from two serious problems: forgetting and difficult low-confidence test cases. In this paper, we propose an open-style skill acquisition approach that contrasts with conventional closed-style training, where recently learned features may overwrite previous ones to minimize overall loss in backpropagation. Instead of compressing all knowledge into a single fixed model, our approach accumulates diverse skills (represented as groups of neurons and connections) during training to reduce the forgetting problem. During inference, the model dynamically selects the most suitable skills for each test sample to maximize prediction confidence, thereby addressing difficult low-confidence cases during inference. We evaluate our approach on multiple benchmark datasets and biomedical image datasets from the MedMNIST benchmark. Experimental results demonstrate consistent performance improvements over state-of-the-art ensemble methods, highlighting the potential of open-style skill acquisition for robust biomedical deep learning.