Sentinel lymph node status prediction using self-attention networks and contrastive learning from routine histology images of primary tumours

Carlos Hernandez-Perez, Veronica Vilaplana, Josep Malvehy, Marc Combalia

Keywords: Semi-supervised contrastive learning, Multiple- Instance learning, Self-attention, Sentinel lymph node status

TL;DR: In this work we use self-supervised contrastive learning and attention networks to create a Deep Sentinel Lymph Node classification pipeline from histology images.

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 $10^7$ to $10^{12}$ 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's performance.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Application: Histopathology

Secondary Subject Area: Interpretability and Explainable AI

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