Interpretation and visualization techniques for deep learning models in medical imaging
DT Huff, AJ Weisman, R Jeraj - Physics in Medicine & Biology, 2021 - iopscience.iop.org
Deep learning (DL) approaches to medical image analysis tasks have recently become
popular; however, they suffer from a lack of human interpretability critical for both increasing …
popular; however, they suffer from a lack of human interpretability critical for both increasing …
A multi-scale CNN and curriculum learning strategy for mammogram classification
Screening mammography is an important front-line tool for the early detection of breast
cancer, and some 39 million exams are conducted each year in the United States alone …
cancer, and some 39 million exams are conducted each year in the United States alone …
Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks
We explore the problem of classification within a medical image data-set based on a feature
vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We …
vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We …
[HTML][HTML] A survey on explainable artificial intelligence (xai) techniques for visualizing deep learning models in medical imaging
The combination of medical imaging and deep learning has significantly improved
diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent …
diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent …
Deep feature–based automatic classification of mammograms
Breast cancer has the second highest frequency of death rate among women worldwide.
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
Early-stage prevention becomes complex due to reasons unknown. However, some typical …
Low-memory neural network training: A technical report
NS Sohoni, CR Aberger, M Leszczynski… - arXiv preprint arXiv …, 2019 - arxiv.org
Memory is increasingly often the bottleneck when training neural network models. Despite
this, techniques to lower the overall memory requirements of training have been less widely …
this, techniques to lower the overall memory requirements of training have been less widely …
Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks
The convolutional neural network (CNN) is a promising technique to detect breast cancer
based on mammograms. Training the CNN from scratch, however, requires a large amount …
based on mammograms. Training the CNN from scratch, however, requires a large amount …
Breast cancer detection using transfer learning in convolutional neural networks
In the US, breast cancer is diagnosed in about 12% of women during their lifetime and it is
the second leading reason for women's death. Since early diagnosis could improve …
the second leading reason for women's death. Since early diagnosis could improve …
Breast microscopic cancer segmentation and classification using unique 4‐qubit‐quantum model
J Amin, M Sharif, SL Fernandes… - Microscopy …, 2022 - Wiley Online Library
The visual inspection of histopathological samples is the benchmark for detecting breast
cancer, but a strenuous and complicated process takes a long time of the pathologist …
cancer, but a strenuous and complicated process takes a long time of the pathologist …
Automated early breast cancer detection and classification system
Early detection of breast cancer is clinically important to reduce the mortality rate. In this
study, a new computer-aided detection (CAD) and classification system is introduced to …
study, a new computer-aided detection (CAD) and classification system is introduced to …