Results 41 to 50 of about 73,359 (292)

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

, 2018
Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size.
A Jungo, AW Awad, B Fischl, BH Menze, CH Lampert, DN Louis, DR Cox, G Wang, J Lao, JJ Griethuysen van, Julián Pérez-Beteta, N Sanai, R Meier, R Tibshirani, RJ Gillies, S Bakas, S Pereira, S Rathore, Spyridon Bakas, Spyridon Bakas, T Hastie, TC Steed, Y Li   +22 more
core   +1 more source

MRI radiomic features are independently associated with overall survival in soft tissue sarcoma [PDF]

, 2019
Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR ...
Ball, Kevin C, Chapman, Tobias R, Combs, Stephanie E, Hippe, Daniel S, Hoff, Michael N, Kim, Edward Y, Macomber, Meghan W, Nyflot, Matthew J, Peeken, Jan C, Pollack, Seth M, Spraker, Matthew B, Wootton, Landon S   +11 more
core   +2 more sources

Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach

Multimodal Technologies and Interaction
Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights.
S. J. Pawan, Matthew Muellner, Xiaomeng Lei, Mihir Desai, Bino Varghese, Vinay Duddalwar, Steven Y. Cen   +6 more
doaj   +1 more source

An Improvement of Survival Stratification in Glioblastoma Patients via Combining Subregional Radiomics Signatures

Frontiers in Neuroscience, 2021
PurposeTo investigate whether combining multiple radiomics signatures derived from the subregions of glioblastoma (GBM) can improve survival prediction of patients with GBM.MethodsIn total, 129 patients were included in this study and split into training
Yang Yang, Yang Yang, Yu Han, Xintao Hu, Wen Wang, Guangbin Cui, Lei Guo, Lei Guo, Xin Zhang   +8 more
doaj   +1 more source

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images [PDF]

, 2018
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer ...
FA Spanhol, G Biau, H Aerts, J Bill, JK Chan, JS Meyer, NA Hamilton, T Araújo, TP Coroller   +8 more
core   +5 more sources

The transformational potential of molecular radiomics

Journal of Medical Radiation Sciences, 2023
Conventional radiomics in nuclear medicine involve hand‐crafted and computer‐assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI‐augmented segmentation and extraction of lower order traditional ...
Geoffrey Currie, K Elizabeth Hawk, Eric Rohren   +2 more
doaj   +1 more source

Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy

iScience, 2023
Summary: To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma.
Shan-Shan Yang, Qing-He Peng, Ai-Qian Wu, Bao-Yu Zhang, Zhi-Qiao Liu, En-Ni Chen, Fang-Yun Xie, Pu-Yun OuYang, Chun-Yan Chen   +8 more
doaj   +1 more source

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

, 2016
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and ...
Bradley, Andrew P., Carneiro, Gustavo, Nascimento, Jacinto, Oakden-Rayner, Luke, Palmer, Lyle   +4 more
core   +2 more sources

The Bionic Radiologist: avoiding blurry pictures and providing greater insights [PDF]

, 2019
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care.
Dewey, Marc, Wilkens, Uta
core   +1 more source

Cardiac Computed Tomography Radiomics [PDF]

Journal of Thoracic Imaging, 2018
Radiologic images are vast three-dimensional data sets in which each voxel of the underlying volume represents distinct physical measurements of a tissue-dependent characteristic. Advances in technology allow radiologists to image pathologies with unforeseen detail, thereby further increasing the amount of information to be processed.
Kolossváry, Márton József, Kellermayer, Miklós, Merkely, Béla Péter, Maurovich-Horvat, Pál   +3 more
openaire   +3 more sources