CountPath: Automating Fragment Counting in Digital Pathology

Quality control of medical images is a critical component of digital pathology, ensuring that diagnostic images meet required standards. A pre-analytical task within this process is the verification of the number of specimen fragments, a process that ensures that the number of fragments on a slide matches the number documented in the macroscopic report. This step is important to ensure that the slides contain the appropriate diagnostic material from the grossing process, thereby guaranteeing the accuracy of subsequent microscopic examination and diagnosis. Traditionally, this assessment is performed manually, requiring significant time and effort while being subject to significant variability due to its subjective nature. To address these challenges, this study explores an automated approach to fragment counting using the YOLOv9 and Vision Transformer models. Our results demonstrate that the automated system achieves a level of performance comparable to expert assessments, offering a reliable and efficient alternative to manual counting. Additionally, we present findings on interobserver variability, showing that the automated approach achieves an accuracy of 86%, which falls within the range of variation observed among experts (82-88%), further supporting its potential for integration into routine pathology workflows.

A CAD System for Colorectal Cancer from WSI: A Clinically Validated Interpretable ML-based Prototype

The integration of Artificial Intelligence (AI) and Digital Pathology has been increasing over the past years. Nowadays, applications of deep learning (DL) methods to diagnose cancer from whole-slide images (WSI) are, more than ever, a reality within different research groups. Nonetheless, the development of these systems was limited by a myriad of constraints regarding the lack of training samples, the scaling difficulties, the opaqueness of DL methods, and, more importantly, the lack of clinical validation. As such, we propose a system designed specifically for the diagnosis of colorectal samples. The construction of such a system consisted of four stages: (1) a careful data collection and annotation process, which resulted in one of the largest WSI colorectal samples datasets; (2) the design of an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations; (3) the development of an effective sampling approach based on the expected severeness of each tile, which decreased the computation cost by a factor of almost 6x; (4) the creation of a prototype that integrates the full set of features of the model to be evaluated in clinical practice. During these stages, the proposed method was evaluated in four separate test sets, two of them are external and completely independent. On the largest of those sets, the proposed approach achieved an accuracy of 93.44%. DL for colorectal samples is a few steps closer to stop being research exclusive and to become fully integrated in clinical practice.