Prostate cancer is a major global health challenge. In this study, we present an approach for the detection and grading of prostate cancer through the semantic segmentation of adenocarcinoma tissues, specifically focusing on distinguishing between Gl...
Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on m...
Adaptive radiotherapy (ART) enhances prostate cancer treatment by accounting for daily anatomical variations, but clinical implementation remains limited due to the need for accurate and efficient auto segmentation; manual corrections after automated...
Journal of chemical information and modeling
Sep 18, 2025
With the increasing discovery of peptide sequences and the growing demand for peptide-targeted drugs, traditional wet-lab experiment methods have become inadequate for peptide function prediction due to their high cost and that they are time consumin...
Journal of chemical information and modeling
Sep 18, 2025
Given the critical roles played by human cytochrome P450 enzymes (CYPs) in drug metabolism, accurately predicting their potential inhibition and induction by drugs and drug candidates is a key objective for improving drug development and safety asses...
BACKGROUND: Deep learning-accelerated single-shot turbo-spin-echo techniques (DL-HASTE) enable single-breath-hold T2-weighted abdominal imaging. However, studies evaluating the image quality of DL-HASTE with and without fat saturation (FS) remain lim...
BACKGROUND: Osteoporosis is a bone disease characterized by reduced bone mineral density and mass, which increase the risk of fragility fractures in patients. Artificial intelligence can mine imaging features specific to different bone densities, sha...
Emotion recognition faces significant challenges in complex real-world environments, particularly under facial occlusion conditions that severely impact traditional deep learning approaches. This research proposes ChildEmoNet, a novel cascaded emotio...
Due to concentrated conflicts, on-ramp merging is an important scenario in the study of new hybrid traffic control. Current research mainly focuses on optimizing the vehicle passage sequence of ramp vehicles merging with mainline vehicles in single-l...
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for ...
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