AIMC Topic: Image Interpretation, Computer-Assisted

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Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Medical image analysis
Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrh...

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

Medical image analysis
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning method...

Classification of melanoma skin Cancer based on Image Data Set using different neural networks.

Scientific reports
This paper aims to address the pressing issue of melanoma classification by leveraging advanced neural network models, specifically basic Convolutional Neural Networks (CNN), ResNet-18, and EfficientNet-B0. Our objectives encompass presenting and eva...

Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index.

Artificial intelligence in medicine
The Anti-Nuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells in the Indirect Immuno-Fluorescence (IIF) assay protocol is considered the gold standard for detecting Connective Tissue Diseases. Computer-assisted systems for HEp-2 ...

Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

Computers in biology and medicine
The morphological diagnosis of thyroid gland neoplasms presents a dual challenge: it requires the expertise of highly trained specialists and considerable time, particularly when evaluating multiple whole slide images (WSIs) from a single patient. Th...

Clinical feasibility of a deep learning approach for conventional and synthetic diffusion-weighted imaging in breast cancer: Qualitative and quantitative analyses.

European journal of radiology
PURPOSE: In this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstr...

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification.

IEEE transactions on cybernetics
Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) ca...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Sensors (Basel, Switzerland)
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnost...

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.

Scientific reports
Brain tumours (BT) affect human health owing to their location. Artificial intelligence (AI) is intended to assist in diagnosing and treating complex diseases by combining technologies like deep learning (DL), big data analytics, and machine learning...

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaqu...