AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 1141 to 1150 of 3033 articles

The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model.

International journal of surgical pathology
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) plat...

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.

Computers in biology and medicine
Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapabl...

Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Breath-holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning-based reconstruction (DLR) of undersampled k-space promises to shorten BHs while prese...

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

European radiology
OBJECTIVES: To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework.

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques.

Computers in biology and medicine
The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image...

Boosted Additive Angular Margin Loss for breast cancer diagnosis from histopathological images.

Computers in biology and medicine
Pathologists use biopsies and microscopic examination to accurately diagnose breast cancer. This process is time-consuming, labor-intensive, and costly. Convolutional neural networks (CNNs) offer an efficient and highly accurate approach to reduce an...

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Computers in biology and medicine
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using he...