A Self-Contrastive Learning Framework for Skin Cancer Detection Using Histological Images
Rocío del Amor, Adrián Colomer, Sandra Morales, Cristian Camilo Pulgarín-Ospina, Liria Terrádez-Mas, José Aneiros‐Fernández, Valery Naranjo · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
Cutaneous spindle cell (CSC) neoplasms are a group of tumors that represent a formidable diagnostic challenge for dermatopathologists. Digital pathology has enabled the application of new methods based on artificial intelligence to reduce the workload of pathologists’ daily practice. In this work, we propose a self-learning framework to detect tumor regions in histological images. The use of a teacher-model paradigm increases the annotated database while avoiding manual annotation. The pre-trained latent space of this model is then used in a second stage by another model to differentiate between leiomyomas (benign cases) and leiomyosarcomas (malignant cases). A contrastive learning approach allows separating the latent space of samples from different classes. This framework was tested on an independent database. This novel approach supposes a step forward in the CSC detection as the obtained results suggest (Acc = 0.90 and 0.8451, respectively).