Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
Big self-supervised models advance medical image classification
Self-supervised pretraining followed by supervised fine-tuning has seen success in image
recognition, especially when labeled examples are scarce, but has received limited attention …
recognition, especially when labeled examples are scarce, but has received limited attention …
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
Acpl: Anti-curriculum pseudo-labelling for semi-supervised medical image classification
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two
challenges: 1) work effectively on both multi-class (eg, lesion classification) and multi-label …
challenges: 1) work effectively on both multi-class (eg, lesion classification) and multi-label …
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 …
Fiba: Frequency-injection based backdoor attack in medical image analysis
In recent years, the security of AI systems has drawn increasing research attention,
especially in the medical imaging realm. To develop a secure medical image analysis (MIA) …
especially in the medical imaging realm. To develop a secure medical image analysis (MIA) …
Lvit: language meets vision transformer in medical image segmentation
Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …
However, the performance of existing medical image segmentation models has been limited …
A multimodal transformer to fuse images and metadata for skin disease classification
G Cai, Y Zhu, Y Wu, X Jiang, J Ye, D Yang - The Visual Computer, 2023 - Springer
Skin disease cases are rising in prevalence, and the diagnosis of skin diseases is always a
challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to …
challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to …