AI applications to medical images: From machine learning to deep learning

I Castiglioni, L Rundo, M Codari, G Di Leo, C Salvatore… - Physica medica, 2021 - Elsevier
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

I Qureshi, J Yan, Q Abbas, K Shaheed, AB Riaz… - Information …, 2023 - Elsevier
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Deep learning for cardiac image segmentation: a review

C Chen, C Qin, H Qiu, G Tarroni, J Duan… - Frontiers in …, 2020 - frontiersin.org
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Deep semantic segmentation of natural and medical images: a review

S Asgari Taghanaki, K Abhishek, JP Cohen… - Artificial Intelligence …, 2021 - Springer
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Weakly supervised machine learning

Z Ren, S Wang, Y Zhang - CAAI Transactions on Intelligence …, 2023 - Wiley Online Library
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Understanding deep networks via extremal perturbations and smooth masks

R Fong, M Patrick, A Vedaldi - Proceedings of the IEEE/CVF …, 2019 - openaccess.thecvf.com
Attribution is the problem of finding which parts of an image are the most responsible for the
output of a deep neural network. An important family of attribution methods is based on …

C-cam: Causal cam for weakly supervised semantic segmentation on medical image

Z Chen, Z Tian, J Zhu, C Li… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Recently, many excellent weakly supervised semantic segmentation (WSSS) works are
proposed based on class activation mapping (CAM). However, there are few works that …

Going deep in medical image analysis: concepts, methods, challenges, and future directions

F Altaf, SMS Islam, N Akhtar, NK Janjua - IEEE Access, 2019 - ieeexplore.ieee.org
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

Prior-aware neural network for partially-supervised multi-organ segmentation

Y Zhou, Z Li, S Bai, C Wang, X Chen… - Proceedings of the …, 2019 - openaccess.thecvf.com
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …