Domain adaptation for medical image analysis: a survey
Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …
from the domain shift problem caused by different distributions between source/reference …
Artificial intelligence and machine learning for medical imaging: A technology review
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …
of disruptive technical advances and impressive experimental results, notably in the field of …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains
Abstract Model generalization capacity at domain shift (eg, various imaging protocols and
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …
Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images
Chest computed tomography (CT) imaging has become indispensable for staging and
managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies …
managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies …
Source free domain adaptation for medical image segmentation with fourier style mining
Unsupervised domain adaptation (UDA) aims to exploit the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled target domain. Existing …
labeled source dataset to solve similar tasks in a new unlabeled target domain. Existing …
Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation
Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography
(CT) scans plays an important role in the quantitative management of patients. Most of the …
(CT) scans plays an important role in the quantitative management of patients. Most of the …
Causal knowledge fusion for 3D cross-modality cardiac image segmentation
Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …
[HTML][HTML] 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 …
Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling
Abstract Domain adaptation typically requires to access source domain data to utilize their
distribution information for domain alignment with the target data. However, in many real …
distribution information for domain alignment with the target data. However, in many real …