Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated wit...
Self-supervised learning aims to learn transferable representations from unlabeled data for downstream tasks. Inspired by masked language modeling in natural language processing, masked image modeling (MIM) has achieved certain success in the field o...
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating...
Self-supervised learning (SSL) has long had great success in advancing the field of annotation-efficient learning. However, when applied to CT volume segmentation, most SSL methods suffer from two limitations, including rarely using the information a...
Electron microscopy (EM) image denoising is critical for visualization and subsequent analysis. Despite the remarkable achievements of deep learning-based non-blind denoising methods, their performance drops significantly when domain shifts exist bet...
The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nano...
Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was intro...
Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning me...
Computer methods and programs in biomedicine
Jan 1, 2025
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods...
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...
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