AIMC Topic: Deep Learning

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scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.

IET systems biology
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often fa...

A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...

End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T/T Mapping.

Magnetic resonance imaging
PURPOSE: To accelerate 3D whole-heart joint T/T mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data...

Deep learning-enhanced hyperspectral imaging for rapid screening of Co-metabolic microplastic-degrading bacteria in environmental samples.

Journal of hazardous materials
Microbial biodegradation of microplastic (MP) emerges as an environmentally benign and highly promising strategy for alleviating MP pollution in the ecosystem. Conventional approaches for screening MP-degrading bacteria use pollutants as the sole car...

On robust learning of memory attractors with noisy deep associative memory networks.

Neural networks : the official journal of the International Neural Network Society
Developing the computational mechanism for memory systems is a long-standing focus in machine learning and neuroscience. Recent studies have shown that overparameterized autoencoders (OAEs) implement associative memory (AM) by encoding training data ...

Computational models for prediction of m6A sites using deep learning.

Methods (San Diego, Calif.)
RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modificat...

Enhancing forensic shoeprint analysis: Application of the Shoe-MS algorithm to challenging evidence.

Science & justice : journal of the Forensic Science Society
Quantitative assessment of pattern evidence is a challenging task, particularly in the context of forensic investigations where the accurate identification of sources and classification of items in evidence are critical. Emerging deep learning approa...

Causal recurrent intervention for cross-modal cardiac image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities...

OrgaMeas: A pipeline that integrates all the processes of organelle image analysis.

Biochimica et biophysica acta. Molecular cell research
Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and labori...

Early operative difficulty assessment in laparoscopic cholecystectomy via snapshot-centric video analysis.

International journal of computer assisted radiology and surgery
PURPOSE: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recogni...