Deep Learning Models Show Generalization Gap in Skin Cancer Classification

Researchers have developed a cascade classification system 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 performed well internally, there was a significant generalization gap when evaluated on external clinical datasets, leading to underestimation of malignancy and calibration issues. A tunable triage threshold in the cascade approach offers controllable sensitivity and better mimics 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 generalization gaps.

RANK_REASON The cluster contains an academic paper detailing a new methodology and evaluation of deep learning models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ConvNeXt-S
• EfficientNetV2-S
• ImageNet
• ISIC Archive
• Melanoscope AI
• Swin-S
• ViT-B/16

• paper
• model release
• safety

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