AIMC Topic: Deep Learning

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Transformer attention-based neural network for cognitive score estimation from sMRI data.

Computers in biology and medicine
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatom...

EnsemPred-ACP: Combining machine and deep learning to improve anticancer peptide prediction.

Computers in biology and medicine
Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of...

Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors.

Computers in biology and medicine
Respiratory and cardiovascular diseases represent a significant global health burden, underscoring the need for innovative, accessible, and cost-effective screening solutions. This study introduces a clinically grounded framework for the early detect...

DeepPerfusion: A comprehensible two-branched deep learning architecture for high-precision blood volume pulse extraction based on imaging photoplethysmography.

Computers in biology and medicine
Imaging photoplethysmography (iPPG) is a contactless approach for the extraction of the blood volume pulsation (BVP). Analyzing the small intensity changes resulting from fluctuations in light absorption in upper skin layers enables BVP extraction. I...

Radiology report generation using automatic keyword adaptation, frequency-based multi-label classification and text-to-text large language models.

Computers in biology and medicine
BACKGROUND: Radiology reports are essential in medical imaging, providing critical insights for diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiolog...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

Journal of neuroradiology = Journal de neuroradiologie
INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving ther...

Content-based X-ray image retrieval using fusion of local neighboring patterns and deep features for lung disease detection.

Radiological physics and technology
This paper introduces a Content-Based Medical Image Retrieval (CBMIR) system for detecting and retrieving lung disease cases to assist doctors and radiologists in clinical decision-making. The system combines texture-based features using Local Binary...

A Novel Two-step Classification Approach for Differentiating Bone Metastases From Benign Bone Lesions in SPECT/CT Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to develop and validate a novel two-step deep learning framework for the automated detection, segmentation, and classification of bone metastases in SPECT/CT imaging, accurately distinguishing malignant from ...

Drug-target interaction/affinity prediction: Deep learning models and advances review.

Computers in biology and medicine
Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate prediction of how...

Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions - a first large-scale multi-institutional clinical trial in Japan.

Breast cancer (Tokyo, Japan)
BACKGROUND: Recently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; however, their effectiveness has not yet been sufficiently validated in Japanese women. In this study, we ...