🤖 AI Summary
In digital pathology quality control, manual verification of tissue fragment counts is time-consuming, highly subjective, and exhibits substantial inter-observer variability (inter-expert agreement: 82–88%), thereby compromising diagnostic reliability. To address this, we propose the first automated fragment counting method integrating YOLOv9 and Vision Transformer architectures, augmented with medical image-specific preprocessing and postprocessing optimizations. Evaluated on real-world clinical whole-slide images, our method achieves an 86% counting accuracy—within the observed range of inter-expert variability. By substantially reducing subjective bias and demonstrating robust performance on routine clinical data, the approach exhibits strong feasibility for clinical deployment. This work establishes a generalizable technical paradigm for end-to-end automated quality control in digital pathology workflows.
📝 Abstract
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.