Our client is a major international pharmaceutical company, which conducts research and development activities related to a wide range of human medical disorders, including mental illness, neurological disorders, anaesthesia and analgesia, gastrointestinal disorders, fungal infection, allergies, and cancer.
The goal of the pilot project was to assess the feasibility of automating the scoring of histology slides. These slides are a way to assess the activity of inflammatory bowel disease. The focus was on Crohn's disease. Scoring requires a trained pathologist and is time-consuming.
On input, we had about 1500 biopsies and metadata. The biopsies had been labelled by an expert pathologist. Slides were stained with hematoxylin and eosin (H&E).
We were to build a system that would automatically assign class labels to new biopsies. The class labels correspond to abnormalities defined by the Global Histology Activity Score (GHAS). This scoring system defines multiple scoring components, but only three of them were in the scope of the project: epithelial damage, infiltration of mononuclear cells in lamina propria (LP), and infiltration of polymorphonuclear cells in LP.
Solving the task involved three subtasks:
Python libraries used: Keras, Tensorflow, openslide, scikit-learn.
We achieved the weighted F1 score of 0.76-0.81 for different scoring components. F1 is a measure of classification accuracy, ranging between 0 and 1.
It has been shown that automating the scoring of histology slides is feasible. Further efforts may improve scoring accuracy and take the system closer to being usable for an automated second opinion.