AIMC Topic: Neural Networks, Computer

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Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

European journal of cancer (Oxford, England : 1990)
IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learnin...

Prediction of hematoma changes in spontaneous intracerebral hemorrhage using a Transformer-based generative adversarial network to generate follow-up CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: To visualize and assess hematoma growth trends by generating follow-up CT images within 24 h based on baseline CT images of spontaneous intracerebral hemorrhage (sICH) using Transformer-integrated Generative Adversarial Networks (GAN).

Diabetic retinopathy classification using a multi-attention residual refinement architecture.

Scientific reports
Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading to blurred vision and even blindness. We propose a multi-attention residual refinement architecture that enhances conventional CNN performance through...

Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.

Scientific reports
The development and refinement of artificial intelligence (AI) and machine learning algorithms have been an area of intense research in radiology and pathology, particularly for automated or computer-aided diagnosis. Whole Slide Imaging (WSI) has eme...

Pulmonary diseases accurate recognition using adaptive multiscale feature fusion in chest radiography.

Scientific reports
Pulmonary disease can severely impair respiratory function and be life-threatening. Accurately recognizing pulmonary diseases in chest X-ray images is challenging due to overlapping body structures and the complex anatomy of the chest. We propose an ...

Deep Learning-aided H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (H-MRS) is a well-established...

FGDN: A Federated Graph Convolutional Network framework for multi-site major depression disorder diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The vast amount of healthcare data is characterized by its diversity, dynamic nature, and large scale. It is a challenge that directly training a Graph Convolutional Neural Network (GCN) in a multi-site dataset poses to protecting the privacy of Majo...

Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.

Scientific reports
Cancer of bone marrow is classified as Acute Lymphoblastic Leukemia (ALL), an abnormal growth of lymphoid progenitor cells. It affects both children and adults and is the most predominant form of infantile cancer. Currently, there has been significan...

Oral cancer detection via Vanilla CNN optimized by improved artificial protozoa optimizer.

Scientific reports
In this study, we propose a new method for oral cancer detection using a modified Vanilla Convolutional Neural Network (CNN) architecture with incorporated batch normalization, dropout regularization, and a customized design structure for the convolu...

A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.

Scientific reports
Effective financial risk management in healthcare systems requires intelligent decision-making that balances treatment quality with cost efficiency. This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowled...