Being able to automate the detection of metastasised cancer in pathological scans with machine learning and deep neural networks is an area of medical imaging and diagnostics with promising potential for clinical usefulness.
Here we explore a particular dataset prepared for this type of analysis and diagnostics. The PCam dataset is a binary classification image dataset containing approximately 300,000 labeled low-resolution images of lymph node sections extracted from digital histopathological scans. Each image is labelled by trained pathologists for the presence of metastasised cancer.
Using a convolutional neural network, transfer learning, and other hyperparameter optimisations, we show how we can predict the occurrence of cancer in this dataset with an accuracy of 98.6%.