AIMC Topic: Deep Learning

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SGcCA: Deciphering Drug-Target Interactions through an End-to-End Model with Spatial and Channel Reconstruction Convolution and Cross-Efficient-Additive Attention.

Journal of chemical information and modeling
Drug-Target Interaction (DTI) prediction is an indispensable process in drug repositioning. Wet-lab experiments for potential DTI identification are reliable but expensive, labor-intensive, and time-consuming. Deep learning demonstrates the superior ...

Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.

BMC oral health
BACKGROUND: To develop a new deep learning model for detecting white spot lesions (WSLs), which are commonly observed in patients undergoing orthodontic treatment, and assess its accuracy.

AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.

Current atherosclerosis reports
PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) r...

A meta-learning framework to mitigate negative transfer in transfer learning applicable to drug design.

Scientific reports
Data sparseness is a major limiting factor for deep machine learning. In the natural sciences, data distributions are heterogeneous. For instance, in chemistry and early-phase drug discovery, compound and molecular property data are typically sparse ...

Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images.

Scientific reports
Renal cell carcinoma (RCC) is one of the typical categories of kidney cancer and is a varied group of malignancies arising from epithelial cells of the kidney parenchyma. RCC has more than ten subtypes. Classification of RCC sub-types is mainly accor...

cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.

Communications biology
Cryo-electron tomography enables the visualization of macromolecular complexes within native cellular environments but is limited by incomplete angular sampling and the maximal electron dose that biological specimens can be exposed to. Here, we devel...

Adaptive heartbeat regulation using double deep reinforcement learning in a Markov decision process framework.

Scientific reports
The erratic nature of cardiac rhythms can precipitate a multitude of pathologies. Consequently, the endeavor to achieve stabilization of the human heartbeat has garnered significant scholarly interest in recent years. In this context, an adaptive non...

A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.

Scientific reports
Retinal diseases are among the leading causes of visual impairment worldwide, where timely diagnosis and management are critical to prevent irreversible vision loss and blindness, especially in regions with limited access to ophthalmologists. While a...

A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.

Scientific reports
Brain tumors are a critical medical challenge, requiring accurate and timely diagnosis to improve patient outcomes. Misclassification can significantly reduce life expectancy, emphasizing the need for precise diagnostic methods. Manual analysis of ex...

Pseudo PET synthesis from CT based on deep neural networks.

Physics in medicine and biology
. Integrated positron emission tomography (PET)/computed tomography (CT) imaging plays a vital role in tumor diagnosis by offering both anatomical and functional information. However, the high cost, limited accessibility of PET imaging and concerns a...