SHERLOCK

Abstract

Whole Slide Images (WSI) are gigantic images (e.g. 100k x 100k pixels) of tissue samples. The goal of SHERLOCK is to detect cancer cells in those tissue samples. We do this by using a pretrained Masked Autoencoder (MAE), from Facebook’s research lab, that we finetune on the PanNuke dataset. The benefit of using an MAE is that unlike supervised learning the WSI’s don’t need to be labeled. This is important because it will save a lot of time and money that would be spent on labeling WSI’s.

Methods

SHERLOCK uses PyTorch as its machine learning framework. We created custom PanNuke Datasets that we used with Facebook’s pre-trained Masked AutoEncoder (MAE). This uses Vision Transformers (ViT’s) to do binary classification on our dataset.

Results

We finetune our model and do binary classification which detects if the patch from the WSI is inflammatory or not.