Protein science : a publication of the Protein Society
May 1, 2025
Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately ...
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...
Purpose To present and evaluate Dafne (deep anatomical federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of radiologic images through federated incremental learning. Materials and M...
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest ...
Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...
Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials...
OBJECTIVES: This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandib...
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...
OBJECTIVES: This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in cone-beam CT (CBCT) data to provide a reliable and efficient support tool for dental implant treatment...
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