Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep ...
IEEE/ACM transactions on computational biology and bioinformatics
Feb 5, 2024
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgrap...
Computer methods and programs in biomedicine
Jan 30, 2024
BACKGROUND AND OBJECTIVE: Symptom descriptions by ordinary people are often inaccurate or vague when seeking medical advice, which often leads to inaccurate preliminary clinical diagnoses. To address this issue, we propose a deep learning model named...
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully s...
Semi-Supervised Learning (SSL) has demonstrated great potential to reduce the dependence on a large set of annotated data, which is challenging to collect in clinical practice. One of the most important SSL methods is to generate pseudo labels from t...
Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibi...
BACKGROUND: Artificial intelligence has the potential to dramatically alter health care by enhancing how we diagnose and treat disease. One promising artificial intelligence model is ChatGPT, a general-purpose large language model trained by OpenAI. ...
Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of ...
Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully auto...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 11, 2024
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...
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