AI in health and medicine
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
Universeg: Universal medical image segmentation
While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
Checklist for evaluation of image-based artificial intelligence reports in dermatology: CLEAR derm consensus guidelines from the international skin imaging …
Importance The use of artificial intelligence (AI) is accelerating in all aspects of medicine and
has the potential to transform clinical care and dermatology workflows. However, to develop …
has the potential to transform clinical care and dermatology workflows. However, to develop …
Guidelines and evaluation of clinical explainable AI in medical image analysis
Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed
decision support from AI and comply with evidence-based medical practice. Applying XAI in …
decision support from AI and comply with evidence-based medical practice. Applying XAI in …
Scribbleprompt: Fast and flexible interactive segmentation for any medical image
Semantic medical image segmentation is a crucial part of both scientific research and
clinical care. With enough labelled data, deep learning models can be trained to accurately …
clinical care. With enough labelled data, deep learning models can be trained to accurately …
Visual explanations for the detection of diabetic retinopathy from retinal fundus images
In medical image classification tasks like the detection of diabetic retinopathy from retinal
fundus images, it is highly desirable to get visual explanations for the decisions of black-box …
fundus images, it is highly desirable to get visual explanations for the decisions of black-box …
A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs
Abstract Background and Objectives Patients with angle-closure glaucoma (ACG) are
asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) …
asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) …
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …
Shared interest... sometimes: Understanding the alignment between human perception, vision architectures, and saliency map techniques
K Morrison, A Mehra, A Perer - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Empirical studies have shown that attention-based architectures outperform traditional
convolutional neural networks (CNN) in terms of accuracy and robustness. As a result …
convolutional neural networks (CNN) in terms of accuracy and robustness. As a result …
Feature Interpretation Using Generative Adversarial Networks (FIGAN): A framework for visualizing a CNN's learned features
Convolutional neural networks (CNNs) are increasingly being explored and used for a
variety of classification tasks in medical imaging, but current methods for post hoc …
variety of classification tasks in medical imaging, but current methods for post hoc …