Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours

Abstract

Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.

ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research

“…The current application of AI in veterinary medicine cover a wide range of topics, such as dental radiograph ( 2 ), colic detection ( 3 ), and mitosis detection in digital pathology ( 4 ). Machine learning (ML), a subset of AI, enables systems to learn from data without being explicitly programmed ( 5 ).…”

Table_1.docx

“…The current application of AI in veterinary medicine cover a wide range of topics, such as dental radiograph (2), colic detection (3), and mitosis detection in digital pathology (4). Machine learning (ML), a subset of AI, enables systems to learn from data without being explicitly programmed (5).…”