AIMC Topic: Image Processing, Computer-Assisted

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Virtual high-count PET image generation using a deep learning method.

Medical physics
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan tim...

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Sensors (Basel, Switzerland)
Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual an...

DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm.

Scanning
OBJECTIVE: A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated.

Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation.

Computational intelligence and neuroscience
To study the effect of computerized tomography (CT) images based on deep learning algorithms on the diagnosis of pulmonary nodules and the effect of radiofrequency ablation (RFA), the -shaped fully convolutional neural network (FCNN) (-Net) was enhan...

Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images.

Computers in biology and medicine
The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architec...

Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Medicina (Kaunas, Lithuania)
Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosi...

Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model.

Computational intelligence and neuroscience
Image segmentation and computer vision are becoming more important in computer-aided design. A computer algorithm extracts image borders, colours, and textures. It also depletes resources. Technical knowledge is required to extract information about ...

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net.

Computer methods and programs in biomedicine
PURPOSE: This paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascul...