AIMC Topic: Deep Learning

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A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Scientific reports
Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis ...

Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.

Scientific reports
Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The ob...

Design of EEG based thought identification system using EMD & deep neural network.

Scientific reports
Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based mes...

Leveraging convolutional neural networks and hashing techniques for the secure classification of monkeypox disease.

Scientific reports
The World Health Organization declared a state of emergency in 2022 because of monkeypox. This disease has raised international concern as it has spread beyond Africa, where it is endemic. The global community has shown attention and solidarity in co...

CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis.

Scientific reports
Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India...

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

IEEE transactions on medical imaging
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifa...

Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation.

IEEE transactions on medical imaging
Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant clinical implications across various medical applications. However, existing methods often exhibit limitations in achieving satisfactory topological performa...

GC: Generalizable Continual Classification of Medical Images.

IEEE transactions on medical imaging
Deep learning models have achieved remarkable success in medical image classification. These models are typically trained once on the available annotated images and thus lack the ability of continually learning new tasks (i.e., new classes or data di...

A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.

IEEE journal of translational engineering in health and medicine
To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver func...

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.

Oral diseases
OBJECTIVE: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classif...