The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown promise in general domains, t...
Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into...
Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using di...
Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein...
The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi...
Learning to Defer (L2D) algorithms improve human-AI collaboration by deferring decisions to human experts when they are likely to be more accurate than the AI model. These can be crucial in high-stakes tasks like fraud detection, where false negative...
PURPOSE: To demonstrate a method of benchmarking the performance of two consecutive software releases of the same commercial artificial intelligence (AI) product to trained human readers using the Personal Performance in Mammographic Screening scheme...
The burgeoning application of Large Language Models (LLMs) in Natural Language Processing (NLP) has prompted scrutiny of their domain-specific knowledge processing, especially in the construction industry. Despite high demand, there is a scarcity of ...
The use of self-supervised learning to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This ...
The increasing digitalization of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical ...