Publications by authors named "Mohammad Hasan Sarhan"
SF-TMN: SlowFast temporal modeling network for surgical phase recognition.
Int J Comput Assist Radiol Surg
May 2024
Purpose: Automatic surgical phase recognition is crucial for video-based assessment systems in surgical education. Utilizing temporal information is crucial for surgical phase recognition; hence, various recent approaches extract frame-level features to conduct full video temporal modeling.
Methods: For better temporal modeling, we propose SlowFast temporal modeling network (SF-TMN) for offline surgical phase recognition that can achieve not only frame-level full video temporal modeling but also segment-level full video temporal modeling.
Article Synopsis
- Context-aware decision support in operating rooms enhances surgical safety and efficiency by utilizing real-time feedback from workflow analysis, but current methods often miss detailed interactions needed for effective AI assistance.
- The paper introduces CholecTriplet2021, a challenge aimed at recognizing surgical action triplets (instrument, verb, target) in laparoscopic videos, using the CholecT50 dataset annotated with such triplet information.
- It presents the challenge's setup, results from various deep learning methods (with mean average precision ranging from 4.2% to 38.1%), and proposes future research directions to improve fine-grained surgical activity recognition in the field of AI-assisted surgery.
Surgical workflow recognition with temporal convolution and transformer for action segmentation.
Int J Comput Assist Radiol Surg
April 2023
Purpose: Automatic surgical workflow recognition enabled by computer vision algorithms plays a key role in enhancing the learning experience of surgeons. It also supports building context-aware systems that allow better surgical planning and decision making which may in turn improve outcomes. Utilizing temporal information is crucial for recognizing context; hence, various recent approaches use recurrent neural networks or transformers to recognize actions.
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