Face detection in the operating room: comparison of state-of-the-art methods and a self-supervised approach.
Fiche publication
Date publication
avril 2019
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GANGI Afshin
Tous les auteurs :
Issenhuth T, Srivastav V, Gangi A, Padoy N
Lien Pubmed
Résumé
Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can indeed help to detect and identify the persons present in the room and also be used to automatically anonymize the data. However, current algorithms trained on natural images do not generalize well to the operating room (OR) images. In this work, we provide a comparison of state-of-the-art face detectors on OR data and also present an approach to train a face detector for the OR by exploiting non-annotated OR images.
Mots clés
Face detection, MVOR-Faces dataset, Operating room, Semi-supervised learning, Visual domain adaptation
Référence
Int J Comput Assist Radiol Surg. 2019 Apr 9;: