A Digital Tool Supporting Pathology Practice and Identifying Leucocytes.

Abstract

The aim of this work is to deliver an all-in-one package that contains both the part where the pathologist can manipulate the data as well as predefined models, altogether with the digital pathology interface with a comprehensive component that provides traceability between the identified leucocytes and the underlying possible outcomes of the potential disease. The aim is to directly provide the number of leucocytes and the mass of the cell, only from the image, with minimal intervention from the pathologist, necessary to have a PoC (Proof of Concept) or a prototype. The model was trained on a dataset of around 20,000 models, and the achieved accuracy was approximately 85%. Approximately 82% of the identified areas of interest, as determined by the models, were true positive predictions. The models correctly identified approximately 89% of the actual positive instances - areas of interest - identified by the pathologist. Approximately 6% of the total actual negative instances were incorrectly classified as positive by the models. The tool provides visual scripting, reducing the learning curve for pathology analysis techniques and offers an intuitive interface for healthcare professionals.