Related papers: Medical artificial intelligence toolbox (MAIT): an…
An important objective in computational biology is the efficient integration of multi-omics data. The task of integration comes with challenges: multi-omics data are most often unpaired (requiring diagonal integration), partially labeled…
Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic…
Integrating multiple observational studies for meta-analysis has sparked much interest. The presented R package WMAP (Weighted Meta-Analysis with Pseudo-Population) addresses a critical gap in the implementation of integrative weighting…
This report outlines work by the Modeling Transformative AI Risk (MTAIR) project, an attempt to map out the key hypotheses, uncertainties, and disagreements in debates about catastrophic risks from advanced AI, and the relationships between…
Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It…
Approaches to fair and ethical AI have recently fell under the scrutiny of the emerging, chiefly qualitative, field of critical data studies, placing emphasis on the lack of sensitivity to context and complex social phenomena of such…
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, and evaluating new…
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care informatics. Risk estimation involves integration of heterogeneous clinical sources having different representation from different…
The use of deep learning in computer vision tasks such as image classification has led to a rapid increase in the performance of such systems. Due to this substantial increment in the utility of these systems, the use of artificial…
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep…
Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deploying AI without safeguards poses significant risks,…
Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are…
Personalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible al- ternative for predicting the most appropriate therapy for individual patients. However, the…
The MAterials Simulation Toolkit (MAST) is a workflow manager and post-processing tool for ab initio defect and diffusion workflows. MAST codifies research knowledge and best practices for such workflows, and allows for the generation and…
Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinct datasets, implementations, and evaluations. This impedes…
Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were…
Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These…
Survival analysis is a widely-used technique for analyzing time-to-event data in the presence of censoring. In recent years, numerous survival analysis methods have emerged which scale to large datasets and relax traditional assumptions…
Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly under domain shifts and rare-class conditions. Deep learning models often struggle with real-world distribution changes, exhibit…
Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system…