AIMC Topic: Deep Learning

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A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
It is well known that most patients with Parkinson's disease (PD) have different degree of movement disorders, such as shuffling, festination and akinetic episodes, which could degenerate the life quality of PD patients. Therefore, it is very useful ...

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Radiology
Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose To compare t...

Generic Isolated Cell Image Generator.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of the biggest challenges of such systems, especially those using state-of-the-art deep learning techni...

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.

Scientific reports
Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the ...

Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.

Scientific reports
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significant...

CR-Unet: A Composite Network for Ovary and Follicle Segmentation in Ultrasound Images.

IEEE journal of biomedical and health informatics
Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consumin...

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

IEEE transactions on medical imaging
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most ...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

IEEE transactions on medical imaging
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast...

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging.

Journal of medical imaging and radiation sciences
Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to wea...