AIMC Topic: Deep Learning

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MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction.

Journal of chemical information and modeling
Molecular carcinogenicity is a crucial factor in the development of cancer, and accurate prediction of it is vital for cancer prevention, treatment, and drug development. In recent years, deep learning has been applied to predict molecular carcinogen...

Ultra-early detection of S100B biomarkers using a nanophotonic biosensor with deep learning quantification: A clinical model based on EDAS patients.

Biosensors & bioelectronics
BACKGROUND: Ultra-early detection of brain injury biomarkers within the critical first hour post-injury remains a major clinical challenge in mild traumatic brain injury (mTBI) management. Conventional platforms (e.g., ELISA) suffer from limited sens...

DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.

Computers in biology and medicine
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...

Explainable deep learning for stratified medicine in inflammatory bowel disease.

Genome biology
Moving from a one-size-fits-all to an individual approach in precision medicine requires a deeper understanding of disease molecular mechanisms. Especially in heterogeneous complex diseases such as inflammatory bowel disease (IBD), better molecular s...

Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data.

Scientific reports
This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound ima...

Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.

Scientific reports
GI diseases are one of the leading causes of morbidity and mortality worldwide, and early and accurate diagnosis is considered to be very important. Traditional methods like endoscopy take time and depend majorly on the judgment of the physician. The...

A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.

Scientific data
Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI mu...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...

Predicting In-Hospital Mortality in Intensive Care Unit Patients Using Causal SurvivalNet With Serum Chloride and Other Causal Factors: Cross-Country Study.

Journal of medical Internet research
BACKGROUND: Incorporating initial serum chloride levels as a prognostic indicator in the intensive care environment has the potential to refine risk stratification and tailor treatment strategies, leading to more efficient use of clinical resources a...

An automated hybrid deep learning framework for paddy leaf disease identification and classification.

Scientific reports
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...