AIMC Topic: Deep Learning

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Predicting Recurrence in Locally Advanced Rectal Cancer Using Multitask Deep Learning and Multimodal MRI.

Radiology. Imaging cancer
Purpose To develop and validate a deep multitask network, MultiRecNet, for fully automatic prediction of disease-free survival (DFS) in patients with neoadjuvant chemoradiotherapy (nCRT)-treated locally advanced rectal cancer (LARC). Materials and Me...

Deep Learning-based Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI.

Radiology. Imaging cancer
Purpose To develop and evaluate a novel deep learning-based approach for registering presurgical MR and whole-mount histopathology (WMHP) images of the prostate. Materials and Methods This retrospective study included patients who underwent prostate ...

Interactive Explainable Deep Learning Model for Hepatocellular Carcinoma Diagnosis at Gadoxetic Acid-enhanced MRI: A Retrospective, Multicenter, Diagnostic Study.

Radiology. Imaging cancer
Purpose To develop an artificial intelligence (AI) model based on gadoxetic acid-enhanced MRI to assist radiologists in hepatocellular carcinoma (HCC) diagnosis. Materials and Methods This retrospective study included patients with focal liver lesion...

Artificial intelligence assistance using deep metric learning vs. object detection in classifying lumbar disc degeneration on magnetic resonance images.

European review for medical and pharmacological sciences
UNLABELLED: OBJECTIVE: This study aimed to assess the performance of an image retrieval system based on the deep metric learning (DML) approach in discriminating between early and late stages of degenerative intervertebral disc degeneration (IDD). MA...

PLM-DBPs: enhancing plant DNA-binding protein prediction by integrating sequence-based and structure-aware protein language models.

Briefings in bioinformatics
DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through hand...

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.

Briefings in bioinformatics
Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines ...

Image-Based Deep Learning Model for Predicting Lymph Node Metastasis in Lung Adenocarcinoma With CT ≤ 2 cm.

Thoracic cancer
BACKGROUND: Lymph node metastasis (LNM) poses a considerable threat to survival in lung adenocarcinoma. Currently, minor resection is the recommended surgical approach for small-diameter lung cancer. The accurate preoperative identification of LNM in...

Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.

Briefings in bioinformatics
Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in mole...

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

HLA
The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of imm...