Skin Cancer Classification

Skin cancer classification research aims to develop accurate and efficient automated systems for diagnosing skin lesions, improving early detection and treatment outcomes. Current efforts focus on leveraging deep learning architectures, such as convolutional neural networks (CNNs), Vision Transformers, and Siamese networks, often incorporating techniques like transfer learning, data augmentation, and attention mechanisms to address class imbalance and improve diagnostic accuracy. These advancements hold significant promise for improving the accessibility and efficiency of skin cancer diagnosis, particularly in resource-constrained settings, and for aiding dermatologists in making more informed decisions.

Papers

April 21, 2022

Multiple EffNet/ResNet Architectures for Melanoma Classification
Jiaqi Xue, Chentian Ma, Li Li, Xuan Wen
Image Classification Patient Data Melanoma Skin Cancer Skin Cancer Classification Melanoma Classification ResNet Architecture Cancer Prediction

February 23, 2022

Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups
Lucy Godson, Navid Alemi, Jeremie Nsengimana, Graham P. Cook, Emily L. Clarke, Darren Treanor, D. Timothy Bishop, Julia Newton-Bishop, Ali Gooya
Melanoma Skin Cancer Skin Cancer Classification Subgroup Discovery Pathology Model Early Prognosis

November 3, 2021

Skin Cancer Classification using Inception Network and Transfer Learning
Priscilla Benedetti, Damiano Perri, Marco Simonetti, Osvaldo Gervasi, Gianluca Reali, Mauro Femminella
Convolutional Neural Network Transfer Learning Classification Code Training Image Dermoscopic Image Skin Cancer Classification Data Classification

Skin Cancer Classification

Papers

Multiple EffNet/ResNet Architectures for Melanoma Classification

Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups

Skin Cancer Classification using Inception Network and Transfer Learning