AIMC Topic: Image Processing, Computer-Assisted

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Deep learning-based free-water correction for single-shell diffusion MRI.

Magnetic resonance imaging
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white...

Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Toxicologic pathology
Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for a...

Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

Biomedical physics & engineering express
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network ...

Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach.

Physics in medicine and biology
A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(-1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise fo...

Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods.

Biomedical physics & engineering express
Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic metho...

Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.

PloS one
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure...

DVPT: Dynamic Visual Prompt Tuning of large pre-trained models for medical image analysis.

Neural networks : the official journal of the International Neural Network Society
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only ta...

DKiS: Decay weight invertible image steganography with private key.

Neural networks : the official journal of the International Neural Network Society
Image steganography, defined as the practice of concealing information within another image. In this paper, we propose decay weight invertible image steganography with private key (DKiS). This model introduces two major advancements into current inve...

Illuminating the unseen: Advancing MRI domain generalization through causality.

Medical image analysis
Deep learning methods have shown promise in accelerated MRI reconstruction but face significant challenges under domain shifts between training and testing datasets, such as changes in image contrasts, anatomical regions, and acquisition strategies. ...

ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburd...