AIMC Topic: Deep Learning

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AlzFormer: Multi-modal framework for Alzheimer's classification using MRI and graph-embedded demographics guided by adaptive attention gating.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Alzheimer's disease (AD) is the most common neurodegenerative progressive disorder and the fifth-leading cause of death in older people. The detection of AD is a very challenging task for clinicians and radiologists due to the complex nature of this ...

Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning.

Radiation oncology (London, England)
BACKGROUND: Knowledge-Based Planning (KBP) pipelines, which integrate machine learning-based models to predict dose distribution, have gained popularity in clinical radiation therapy. However, for patients with specific requirements, the trained mode...

Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention.

Scientific reports
In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei...

Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China.

Scientific reports
As a key ecological parameter, NPP measures the photosynthetic efficiency of plants in capturing atmospheric carbon. With the warming of the climate, extreme temperature events are frequent, which has exerted a profound influence on NPP. Previous stu...

A comprehensive deep learning approach to improve enchondroma detection on X-ray images.

Scientific reports
An enchondroma is a benign neoplasm of mature hyaline cartilage that proliferates from the medullary cavity toward the cortical bone. This results in the formation of a significant endogenous mass within the medullary cavity. Although enchondromas ar...

Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.

Scientific data
Chronic knee osteoarthritis pain significantly impacts patients' quality of life and motor function. While motor imagery (MI)-based brain-computer interface (BCI) systems have shown promise in rehabilitation, their application to lower-limb condition...

Deep learning approach for screening neonatal cerebral lesions on ultrasound in China.

Nature communications
Timely and accurate diagnosis of severe neonatal cerebral lesions is critical for preventing long-term neurological damage and addressing life-threatening conditions. Cranial ultrasound is the primary screening tool, but the process is time-consuming...

Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis.

PloS one
OBJECTIVE: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. This study aims to explore the correlation between the spatial distribution of lymphocyte aggregates and the prognosis of MIBC using deep learning.

Classification and quantification of sesame oil in edible oils and adulterated mixtures using H NMR spectroscopy combined with multivariate, machine learning, and deep learning models.

Food chemistry
Sesame oil is often adulterated with cheaper oils, necessitating accurate authentication and quantification methods. This study investigates the performance of AI-based models using H NMR spectral data for edible oil classification and sesame oil qua...

MMFi-DPBML: Multi-molecular fingerprint feature fusion for predicting ingredient-target interactions in traditional Chinese medicine.

Journal of ethnopharmacology
RESEARCH PURPOSE: This study proposes MMFi-DPBML, a deep learning framework that in-tegrates multi-molecular fingerprint features for predicting ingredient-target interactions (ITIs) in traditional Chinese medicine (TCM). By capturing di-verse struct...