37 citations · 100 across the 9 of their papers we have counts for
7 papers · 1 filter
Focused Decoding Enables 3D Anatomical Detection by Transformers
Bastian Wittmann, Fernando Navarro, Suprosanna Shit +1
Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation…
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…
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
Giles Tetteh, Fernando Navarro, Johannes Paetzold +3
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injur…
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…
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…
Webly Supervised Learning for Skin Lesion Classification
Fernando Navarro, Sailesh Conjeti, Federico Tombari +1
Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the f…
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
Magdalini Paschali, Sailesh Conjeti, Fernando Navarro +1
In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also i…