AI Article Synopsis
- * The model was trained on an image-tile dataset without human-drawn bounding boxes and was evaluated against a manually-annotated dataset from various institutions, showing competitive average precision scores compared to neuropathology experts.
- * It offers rapid scoring capabilities, enabling analysis of WSIs in minutes on standard workstations, making it a practical tool for pathologists without needing specialized hardware like GPUs.
Article Abstract
Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathy (CAA). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that approximately correlated with gold-standard human CERAD-like WSI scoring (p = 0.07 ± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290693 | PMC |
| http://dx.doi.org/10.1038/s42003-023-05031-6 | DOI Listing |
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