Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
Vision transformer architecture and applications in digital health: a tutorial and survey
The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …
plays an important role in digital health applications. Medical images account for 90% of the …
Gazegnn: A gaze-guided graph neural network for chest x-ray classification
Eye tracking research is important in computer vision because it can help us understand
how humans interact with the visual world. Specifically for high-risk applications, such as in …
how humans interact with the visual world. Specifically for high-risk applications, such as in …
Eye-gaze-guided vision transformer for rectifying shortcut learning
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural
networks from learning meaningful and useful representations, thus jeopardizing the …
networks from learning meaningful and useful representations, thus jeopardizing the …
Enhancing modality-agnostic representations via meta-learning for brain tumor segmentation
In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …
in practice, not all modalities may be available during inference or even training. Previous …
An efficient medical image classification network based on multi-branch CNN, token grouping Transformer and mixer MLP
S Liu, L Wang, W Yue - Applied Soft Computing, 2024 - Elsevier
In recent years, medical image classification techniques based on deep learning have made
remarkable achievements, but most of the current models sacrifice the efficiency of the …
remarkable achievements, but most of the current models sacrifice the efficiency of the …
A comprehensive review of generative AI in healthcare
Y Shokrollahi, S Yarmohammadtoosky… - arXiv preprint arXiv …, 2023 - arxiv.org
The advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across
various sectors, notably in healthcare. Among the significant developments in this field are …
various sectors, notably in healthcare. Among the significant developments in this field are …
Jpg-jointly learn to align: Automated disease prediction and radiology report generation
Automated radiology report generation aims to generate paragraphs that describe fine-
grained visual differences among cases, especially those between the normal and the …
grained visual differences among cases, especially those between the normal and the …
Revisiting computer-aided tuberculosis diagnosis
Tuberculosis (TB) is a major global health threat, causing millions of deaths annually.
Although early diagnosis and treatment can greatly improve the chances of survival, it …
Although early diagnosis and treatment can greatly improve the chances of survival, it …
Artificial intelligence for the analysis of workload-related changes in radiologists' gaze patterns
Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of
which increases at the end of work shifts. In this study, we run an experiment to investigate if …
which increases at the end of work shifts. In this study, we run an experiment to investigate if …