AIMC Topic: Image Processing, Computer-Assisted

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Reliability of uncertainty quantification methods for deep learning auto-segmentation in head and neck organs at risk.

Physics in medicine and biology
Deep learning auto-segmentation has greatly advanced contouring in radiotherapy. However, quality assurance remains necessary due to performance fluctuation among individual patients. This manual process reintroduces variability and partially reduces...

Res-MoCoDiff: residual-guided diffusion models for motion artifact correction in brain MRI.

Physics in medicine and biology
Motion artifacts (ARTs) in brain magnetic resonance imaging (MRI), mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these ARTs, including repeated acquisitions or motion trackin...

A modified deep learning approach for seminal vesicle region localization in prostate MRI.

Scientific reports
The seminal vesicle region plays a crucial role in male reproductive health, and its accurate evaluation is essential for diagnosing infertility and carcinoma. Magnetic resonance imaging (MRI) is the primary modality for assessment; however, manual e...

Efficient fusion transformer model for accurate classification of eye diseases.

Scientific reports
The automatic diagnosis model of medical image based on deep learning can improve the diagnosis efficiency and reduce the diagnosis cost. At present, there is a lack of research on special artificial intelligence models for medical image analysis of ...

Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.

Scientific reports
This study presents an automated system using Convolutional Neural Networks (CNNs) for segmenting FLAIR Magnetic Resonance Imaging (MRI) images to aid in the diagnosis of Multiple Sclerosis (MS). The dataset included 103 patients from Imam Khomeini H...

Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.

BMC oral health
BACKGROUND: The aim of this systematic review is to compare the efficacy of convolutional neural networks (CNN) and Vision Transformers (ViT) in the field of dental imaging, in order to examine in depth the potential, advantages, and limitations of b...

Computer vision assisted deep transfer learning model for accurate grading of renal cell carcinoma from kidney histopathology images.

Scientific reports
Renal cell carcinomas (RCCs) are the seventh most widespread histological cancer. Around 40% of patients die in RCC due to the disease development. Thus, this tumour is the most lethal malignant urological tumour. The histopathologic classification o...

A geography of indoors for analyzing global ways of living using computer vision.

Scientific reports
Globalization is claimed to have a homogenizing effect, reducing pronounced local cultural differences. Indoor living spaces are among the most vivid expressions of local culture, yet they remain underexplored in this context. Our visual AI framework...

Fast water/fatand PDFF mapping via multiple overlapping-echo detachment acquisition and deep learning reconstruction.

Physics in medicine and biology
Rapid and accurate quantitative assessment of muscle tissue characteristics is valuable for the diagnosis and monitoring of neuromuscular diseases (NMDs). Quantitative magnetic resonance imaging (MRI) enables non-invasive assessment of muscle patholo...

Machine and deep learning applied to medical microwave imaging: a scoping review from reconstruction to classification.

Progress in biomedical engineering (Bristol, England)
Microwave imaging (MWI) is a promising modality due to its non-invasive nature and lower cost compared to other medical imaging techniques. These characteristics make it a potential alternative to traditional imaging techniques. It has various medica...