78 citations · 82 across the 8 of their papers we have counts for
8 papers
Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery
Muhammad Asad, Emanuele Colleoni, Pritesh Mehta +7
Purpose: Monocular depth estimation (MDE) is vital for scene understanding in minimally invasive surgery (MIS). However, endoscopic video sequences are often contaminated by smoke,…
Graph Neural Networks for Surgical Scene Segmentation
Yihan Li, Nikhil Churamani, Maria Robu +2
Purpose: Accurate identification of hepatocystic anatomy is critical to preventing surgical complications during laparoscopic cholecystectomy. Deep learning models often struggle w…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
Yunguan Fu, Maria R. Robu, Bongjin Koo +6
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition…
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
Micha Pfeiffer, Isabel Funke, Maria R. Robu +12
In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, c…
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Neil P. Oxtoby, Fabio S. Ferreira, Agoston Mihalik +12
We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions acr…