@conference {cHernandeza, title = {Sentinel lymph node status prediction using self-attention networks and contrastive learning from routine histology images of primary tumours}, booktitle = {Medical Imaging with Deep Learning MIDL 2022}, year = {2022}, month = {07/2022}, abstract = {

Deep learning-based computational pathology approaches are becoming increasingly prominent in histopathology image analysis. However, these images typically come with drawbacks that hamper automatic analysis, which include: labeled sample scarcity or the extremely large size of the images (ranging from\ \ to\ \ pixels). Nonetheless, they have proven to be a powerful tool for diagnosis and risk prevention. One such prevention is reducing the number of patients who undergo surgeries that do not benefit them. This study develops a pipeline for predicting sentinel lymph node (SLN) metastasis non-invasively from digitised Whole Slide Images (WSI) of primary melanoma tumours. Furthermore, we combine the use of a weakly supervised architecture with self-supervised contrastive pre-training. We experimentally demonstrate that 1) the use of self-attention improves sentinel lymph node status prediction and 2) self-supervised contrastive learning improves the quality of the learned representations compared to a standard ImageNet pre-training, which boosts the model{\textquoteright}s performance.

}, author = {Carlos Hernandez and Marc Combalia and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cHernandez22, title = {Sentinel lymph node status prediction with self-attention neural networks using histologies of primary melanoma tumours}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Carlos Hernandez and Ver{\'o}nica Vilaplana and Marc Combalia and Sergio Garc{\'\i}a and Sebastian Podlipnik and Julio Burgos and Susana Puig and Josep Malvehy} }