Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification.
arXiv:2608. 11280v1 Announce Type: cross Abstract: Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models.
By Rofiqul Islam, Lilatul Ferdouse
arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.
By Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev, Oleg I. Samovarov
Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated images, especially for underrepresented skin tones and rare diseases, impedes progress toward measurably fair methods. We introduce cgDDI (Controllable Generation of Diverse Dermatological Imagery), a hybrid framework that (1) synthesizes realistic healthy skin samples without disturbing other input properties, (2) maps single-sample rare lesions onto novel skin-tones and locations non-parametrically, and (3) allows for efficient parametric generation with as few as 10 training samples.
arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.
By Haiyang Wang, Luca Mainardi
Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology , dermatology and pathology . Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently.
By Google AI