Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation
A new research paper explores cascade classification for dermoscopic images of skin neoplasms, comparing various deep learning architectures like ViT-B/16, Swin-S, ConvNeXt-S, and EfficientNetV2-S. The study found that while models perform well internally, there's a significant generalization gap when applied to independent clinical datasets, leading to drops in performance and calibration issues. The proposed cascade approach, with a tunable triage threshold, offers better sensitivity control and aligns with clinical differential-diagnosis logic, though external validation and recalibration are crucial before deployment. AI
IMPACT Highlights the critical need for external validation and recalibration of AI models in medical imaging to bridge the generalization gap.