AIMC Topic: Deep Learning

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MRI-based 2.5D deep learning and radiomics effectively predicted microvascular invasion and Ki-67 expression in hepatocellular carcinoma.

PloS one
OBJECTIVE: To develop and validate an integrated 2.5D deep learning (DL) and Radiomics model using gadoxetic acid-enhanced MRI hepatobiliary phase (HBP) images combined with clinical features for preoperative prediction of microvascular invasion (MVI...

Obscured-ensemble models for genomic prediction.

PloS one
Genomic Prediction (GP) uses dense whole-genome marker sets from lines of a crop to predict agronomic traits for untested genotypes. In recent years, deep learning (DL) approaches for genomic prediction have demonstrated state-of-the-art results. How...

Deep Learning-Decoded Raman Spectroscopy for Hour-Scale iPSC Pluripotency Assessment via Lipid-Protein Biomarkers.

Analytical chemistry
Rapid and label-free evaluation of induced pluripotent stem cell (iPSC) pluripotency is critical for advancing regenerative medicine and clinical applications. Although traditional genomics- and proteomics-based pluripotency assessment methods are re...

RAPID-Net: Accurate Pocket Identification for Binding-Site-Agnostic Docking.

Journal of chemical information and modeling
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for accurate prediction of binding poc...

Self-supervised learning with a contrastive VideoMoCo framework for Saudi Arabic sign language recognition using 3D convolutional networks.

Scientific reports
Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum ...

An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning.

Scientific reports
In many countries, locusts have significantly harmed agricultural production. To prevent their spread, the Agriculture Robot (Agribot) with cutting-edge technologies like the Internet of Things (IoT) and Machine Learning (ML) can be a possible soluti...

DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.

Scientific reports
Glaucoma remains one of the primary causes of irreversible blindness, characterized by gradual damage to the optic nerve, which often goes undetected until advanced stages. Accurate and early diagnosis depends heavily on precise segmentation of the o...

Multi-stage knowledge distillation with layer fusion-based deep learning approach for skin cancer classification.

Scientific reports
Skin cancer is one of the most common types of cancer globally, caused by prolonged exposure to the sun's UV rays. Despite recent developments in research, early diagnosis, prevention, and treatment, skin cancer remains a significant health concern. ...

Estimating 10-Year Cardiovascular Disease Risk in Primary Prevention Using UK Electronic Health Records and a Hybrid Multitask BERT Model: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of preventable morbidity and mortality, highlighting the need for early risk stratification in primary prevention. Traditional Cox models assume proportional hazards and linear effects,...

Real-time deep learning for tumor segmentation and tool tracking: development and validation of an AI navigation system in vacuum-assisted breast biopsy.

World journal of surgical oncology
BACKGROUND: Vacuum-assisted breast biopsy (VABB) is a widely adopted minimally invasive technique for the diagnosis and treatment of breast lesions. However, the procedure heavily relies on real-time ultrasound guidance, posing significant challenges...