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Image Interpretation, Computer-Assisted

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Distance guided generative adversarial network for explainable medical image classifications.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Despite the potential benefits of data augmentation for mitigating data insufficiency, traditional augmentation methods primarily rely on prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-...

A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers.

Magnetic resonance imaging
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening rout...

Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

Computers in biology and medicine
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a nov...

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimens...

Evaluating retinal blood vessels for predicting white matter hyperintensities in ischemic stroke: A deep learning approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic stroke patients with a high burden of White Matter Hyperintensities (WMH) using Nuclear Magnetic Resonanc...

Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Microvascular research
OBJECTIVE: To evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SC...

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method.

International journal of medical informatics
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK a...

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

International journal of computer assisted radiology and surgery
PURPOSE: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed s...

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Abdominal radiology (New York)
Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques,...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Medical image analysis
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haemat...