A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine
Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …
years and is being implemented in society. The medical field is no exception, and the clinical …
Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging
M Perkonigg, J Hofmanninger, CJ Herold… - Nature …, 2021 - nature.com
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine
learning has increasingly gained relevance because it captures features of disease and …
learning has increasingly gained relevance because it captures features of disease and …
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
[HTML][HTML] On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images
Deep learning-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
COVID-19 automatic diagnosis with radiographic imaging: Explainable attention transfer deep neural networks
Researchers seek help from deep learning methods to alleviate the enormous burden of
reading radiological images by clinicians during the COVID-19 pandemic. However …
reading radiological images by clinicians during the COVID-19 pandemic. However …
Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence
With the recent advancement of novel biomedical technologies such as high-throughput
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …
CyCMIS: Cycle-consistent Cross-domain Medical Image Segmentation via diverse image augmentation
R Wang, G Zheng - Medical Image Analysis, 2022 - Elsevier
Abstract Domain shift, a phenomenon when there exists distribution discrepancy between
training dataset (source domain) and test dataset (target domain), is very common in …
training dataset (source domain) and test dataset (target domain), is very common in …
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Anatomical segmentation is a fundamental task in medical image computing, generally
tackled with fully convolutional neural networks which produce dense segmentation masks …
tackled with fully convolutional neural networks which produce dense segmentation masks …