AIMC Topic: Image Processing, Computer-Assisted

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CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation.

Physics in medicine and biology
Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. O...

Medical applications of generative adversarial network: a visualization analysis.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning (DL) is one of the latest approaches to artificial intelligence. As an unsupervised DL method, a generative adversarial network (GAN) can be used to synthesize new data.

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static a...

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

International journal of computer assisted radiology and surgery
PURPOSE: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. T...

A visual-language foundation model for pathology image analysis using medical Twitter.

Nature medicine
The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical ...

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Magnetic resonance in medicine
PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, numbe...

A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.

Journal of digital imaging
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images ...

Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of or...