Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network

• URL: http://arxiv.org/abs/2501.02618v1
• Date: Sun, 05 Jan 2025 18:18:52 GMT
• Title: Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network
• Authors: Sanya Sinha, Michal Balazia, Francois Bremond,
• Abstract summary: This paper presents a deep learning model for real-time identification of surgical instruments in cataract surgery videos. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-d Efficient Layer Aggregation Network (Go-ELAN) The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images.
• Score: 1.053373860696675
• License:
• Abstract: Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-Optimized Efficient Layer Aggregation Network (Go-ELAN) to address the information bottleneck problem, enhancing Minimum Average Precision (mAP) at higher Non-Maximum Suppression Intersection over Union (NMS IoU) scores. The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images with 10 instrument classes, demonstrating the effectiveness of the proposed model.

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