AIMC Topic: Image Interpretation, Computer-Assisted

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Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics.

IEEE transactions on cybernetics
The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in...

Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and - Fitter.

Computational and mathematical methods in medicine
Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and r...

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach.

Scientific reports
The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device's built-in softwa...

A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.

BMC neurology
BACKGROUND: Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promis...

Robustness of convolutional neural networks in recognition of pigmented skin lesions.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin c...

Universal adversarial attacks on deep neural networks for medical image classification.

BMC medical imaging
BACKGROUND: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-...

PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation.

BMC bioinformatics
BACKGROUND: With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the su...

Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BMC medical imaging
BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able ...

FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy.

BioMed research international
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segm...

Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel o...