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AI models show generalization gap in skin cancer classification

By PulseAugur Editorial · [2 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a new methodology and evaluation of AI models for a specific task.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev, Oleg I. Samovarov ·

    Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation

    arXiv:2606.13135v1 Announce Type: cross Abstract: Purpose. To compare deep learning architectures and classification schemes for dermoscopic images of skin neoplasms and assess their generalization on transfer from open international datasets to independent clinical datasets of R…

  2. arXiv cs.CV TIER_1 English(EN) · Oleg I. Samovarov ·

    Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation

    Purpose. To compare deep learning architectures and classification schemes for dermoscopic images of skin neoplasms and assess their generalization on transfer from open international datasets to independent clinical datasets of Russian practice. Methods. Four architectures (ViT-…

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