AIMC Topic: Deep Learning

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scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.

Genome biology
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensio...

Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.

Scientific reports
Nasal stenosis in bulldogs significantly impacts their quality of life, making early diagnosis crucial for effective treatment. This study developed an automated deep learning model to classify the severity of nasal stenosis using 1020 images of bull...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

Nature communications
RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale w...

[Nobel Prize in physics 2024 : John J. Hopfield and Geoffrey E. Hinton. From Hopfield and Hinton to AlphaFold: The 2024 Nobel Prize honors the pioneers of deep learning].

Medecine sciences : M/S
On October 8, 2024, the Nobel Prize in Physics was awarded to John J. Hopfield, professor at Princeton University, and Geoffrey E. Hinton, professor at the University of Toronto, for their "fundamental discoveries that made possible machine learning ...

Artificial intelligence in chronic kidney disease management: a scoping review.

Theranostics
Chronic kidney disease (CKD) is a major public health problem worldwide associated with cardiovascular disease, renal failure, and mortality. To effectively address this growing burden, innovative solutions to management are urgently required. We co...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

PloS one
Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particularly in the context of neuropathological states. Despite its importance, the application of advanced i...

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method.

PloS one
Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for early detection. Typically, AI-based glaucoma screening relies on fundus imaging. To tackle the resource and time challenges in glaucoma screening with...

Automated Detection of Microcracks Within Second Harmonic Generation Images of Cartilage Using Deep Learning.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks in its collagen network from low-energy impacts previously considered non-injurious. These microcracks may propagate under cyclic loading, impairing ca...

An extragradient and noise-tuning adaptive iterative network for diffusion MRI-based microstructural estimation.

Medical image analysis
Diffusion MRI (dMRI) is a powerful technique for investigating tissue microstructure properties. However, advanced dMRI models are typically complex and nonlinear, requiring a large number of acquisitions in the q-space. Deep learning techniques, spe...

Enhancing skin disease classification leveraging transformer-based deep learning architectures and explainable AI.

Computers in biology and medicine
Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating the classification of these diseases is essential for supporting timely and accurate diagnoses. This study leverages Vision Transformers,...