11 citations · 15 across the 8 of their papers we have counts for
8 papers
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
Muhammad Abdullah Jamal, Omid Mohareri
Surgical scene understanding is a key technical component for enabling intelligent and context aware systems that can transform various aspects of surgical interventions. In this w…
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness
Idris Hamoud, Alexandros Karargyris, Aidean Sharghi +2
Semantic segmentation and activity classification are key components to creating intelligent surgical systems able to understand and assist clinical workflow. In the Operating Room…
AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
Ali Mottaghi, Mohammad Abdullah Jamal, Serena Yeung +1
Semi-supervised domain adaptation (SSDA) presents a critical hurdle in computer vision, especially given the frequent scarcity of labeled data in real-world settings. This scarcity…
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Dimitrios Psychogyios, Emanuele Colleoni, Beatrice Van Amsterdam +47
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to de…
M3D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding
Muhammad Abdullah Jamal, Omid Mohareri
We present a new pre-training strategy called M3D (ulti-odal asked ) built based on Multi-modal masked autoencode…
SurgMAE: Masked Autoencoders for Long Surgical Video Analysis
Muhammad Abdullah Jamal, Omid Mohareri
There has been a growing interest in using deep learning models for processing long surgical videos, in order to automatically detect clinical/operational activities and extract me…