AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 1 to 10 of 3033 articles

Interpretable deep learning for enhanced multi-class classification of gastrointestinal endoscopic images.

Biomedical physics & engineering express
Gastrointestinal (GI) endoscopy serves as a vital tool for assessing the GI tract and diagnosing related disorders. Recent progress in deep learning has shown significant improvements in identifying anomalies using sophisticated models and data augme...

Optimizing and evaluating robustness of AI for brain metastasis detection and segmentation via loss functions and multi-dataset training.

Biomedical physics & engineering express
. Accurate detection and segmentation of brain metastases (BM) from MRI are critical for the appropriate management of cancer patients. This study investigates strategies to enhance the robustness of artificial intelligence (AI)-based BM detection an...

PGMNet: a polyp segmentation network based on bit-plane slicing and multi-scale adaptive fusion.

Biomedical physics & engineering express
Accurate detection and segmentation of polyps during colonoscopy are of great significance for the early prevention and treatment of colorectal cancer. However, due to the considerable variations in polyp size and shape, as well as their blurred boun...

Lightweight Vision Transformer with transfer learning for interpretable Alzheimer's disease severity assessment.

Scientific reports
Early and reliable diagnostic tools are critical for slowing the progression of Alzheimer's disease (AD), a neurodegenerative disorder characterized by memory loss and cognitive decline. This study introduces, ViTTL, lightweight deep learning framewo...

PRCnet: An efficient model for automatic detection of brain tumor in MRI images.

PloS one
Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consu...

An Interpretable Hybrid AI Model for Breast Fine Needle Aspiration Cytology Image Classification.

Journal of medical systems
While Fine needle aspiration cytology (FNAC) and mammography are both used to diagnose breast lesions, FNAC is generally more accurate than mammograms for predicting breast cancer. It is also gaining popularity as an early detection tool due to its r...

A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection.

Scientific reports
This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The s...

Qualitative and quantitative assessment of accelerated liver diffusion-weighted imaging using deep-learning reconstruction in oncologic patients.

BMC medical imaging
BACKGROUND: Deep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patient...