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 …

A multi-scale CNN and curriculum learning strategy for mammogram classification

W Lotter, G Sorensen, D Cox - Deep Learning in Medical Image Analysis …, 2017 - Springer
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 …

Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks

B Kieffer, M Babaie, S Kalra… - … conference on image …, 2017 - ieeexplore.ieee.org
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 …

[HTML][HTML] A survey on explainable artificial intelligence (xai) techniques for visualizing deep learning models in medical imaging

D Bhati, F Neha, M Amiruzzaman - Journal of Imaging, 2024 - mdpi.com
The combination of medical imaging and deep learning has significantly improved
diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent …

Deep feature–based automatic classification of mammograms

R Arora, PK Rai, B Raman - Medical & biological engineering & computing, 2020 - Springer
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 …

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 …

Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks

S Guan, M Loew - Journal of Medical Imaging, 2019 - spiedigitallibrary.org
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 …

Breast cancer detection using transfer learning in convolutional neural networks

S Guan, M Loew - 2017 IEEE applied imagery pattern …, 2017 - ieeexplore.ieee.org
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 …

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 …

Automated early breast cancer detection and classification system

AA Hekal, A Elnakib, HED Moustafa - Signal, Image and Video Processing, 2021 - Springer
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 …