User profiles for "author:Ashirbani Saha"
Ashirbani SahaAssistant Professor, Department of Oncology, McMaster University Verified email at uwindsor.ca Cited by 2801 |
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
[HTML][HTML] Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to mapping the structure and function of the brain …
offer a non-invasive approach to mapping the structure and function of the brain …
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors
Purpose To review features used in MRI radiomics of breast cancer and study the inter‐
reader stability of the features. Methods We implemented 529 algorithmic features that can …
reader stability of the features. Methods We implemented 529 algorithmic features that can …
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which
are associated with shape features. In this study, we propose a fully automatic way to …
are associated with shape features. In this study, we propose a fully automatic way to …
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing
Background and purpose Convolutional neural networks (CNN s) are commonly used for
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent …
Purpose To determine whether a multivariate machine learning-based model using
computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic …
computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic …
[HTML][HTML] A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Background Recent studies showed preliminary data on associations of MRI-based imaging
phenotypes of breast tumours with breast cancer molecular, genomic, and related …
phenotypes of breast tumours with breast cancer molecular, genomic, and related …
Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
Deep learning for identifying radiogenomic associations in breast cancer
Rationale and objectives To determine whether deep learning models can distinguish
between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic …
between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic …
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional …
Recent studies identified distinct genomic subtypes of lower-grade gliomas that could
potentially be used to guide patient treatment. This study aims to determine whether there is …
potentially be used to guide patient treatment. This study aims to determine whether there is …