Search Results for author: Hugo J. W. L. Aerts

Found 4 papers, 1 papers with code

Deep Learning-based Assessment of Hepatic Steatosis on chest CT

no code implementations4 Feb 2022 Zhongyi Zhang, Jakob Weiss, Jana Taron, Roman Zeleznik, Michael T. Lu, Hugo J. W. L. Aerts

A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model.

Computed Tomography (CT) Liver Segmentation

Deep learning-based detection of intravenous contrast in computed tomography scans

2 code implementations16 Oct 2021 Zezhong Ye, Jack M. Qian, Ahmed Hosny, Roman Zeleznik, Deborah Plana, Jirapat Likitlersuang, Zhongyi Zhang, Raymond H. Mak, Hugo J. W. L. Aerts, Benjamin H. Kann

The fine-tuned model on chest CTs yielded an AUC: 1. 0 for the internal validation set (n = 53), and AUC: 0. 980 for the external validation set (n = 402).

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

no code implementations24 Mar 2017 Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Hugo J. W. L. Aerts, Nader Khaouam, Phuc Felix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem, Jan Seuntjens, Issam El Naqa

In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer.

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