24 citations · 24 across the 4 of their papers we have counts for
6 papers
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Fernando Navarro, Guido Sasahara, Suprosanna Shit +4
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For in…
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Fernando Navarro, Christopher Watanabe, Suprosanna Shit +4
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pre…
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
Daniel M. Lang, Jan C. Peeken, Stephanie E. Combs +2
We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets we used a transfer learning approach.…
Deep Reinforcement Learning for Organ Localization in CT
Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter +3
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional im…
Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification
Phawis Thammasorn, Daniel Hippe, Wanpracha Chaovalitwongse +6
Deep representation learning using triplet network for classification suffers from a lack of theoretical foundation and difficulty in tuning both the network and classifiers for pe…
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Fernando Navarro, Suprosanna Shit, Ivan Ezhov +5
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planni…