AIMC Topic: Deep Learning

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Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristi...

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them le...

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

IEEE/ACM transactions on computational biology and bioinformatics
Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been pr...

SSP-Net: A Siamese-Based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, unpaired medical image enhancement is one of the important topics in medical research. Although deep learning-based methods have achieved remarkable success in medical image enhancement, such methods face the challenge of low-quality traini...

Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital health...

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins.

IEEE/ACM transactions on computational biology and bioinformatics
With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accu...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

IEEE/ACM transactions on computational biology and bioinformatics
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it i...

A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression.

IEEE/ACM transactions on computational biology and bioinformatics
Pneumonia is one of the largest causes of death in the world. Deep learning techniques can assist doctors to detect the areas of pneumonia in the chest X-rays images. However, existing methods lack sufficient consideration for the large variation sca...

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This...

An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics
Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges o...