AIMC Topic: Image Processing, Computer-Assisted

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Patch-based system for Classification of Breast Histology images using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost...

Technical and clinical overview of deep learning in radiology.

Japanese journal of radiology
Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep ...

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks.

GigaScience
BACKGROUND: Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the ...

Nasal cytology with deep learning techniques.

International journal of medical informatics
BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driv...

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

NeuroImage
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce...

Improvement of image quality at CT and MRI using deep learning.

Japanese journal of radiology
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for impr...

MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm.

Asian Pacific journal of cancer prevention : APJCP
Generally the segmentation refers, the partitioning of an image into smaller regions to identify or locate the region of abnormality. Even though image segmentation is the challenging task in medical applications, due to contrary image, local observa...

Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier.

Asian Pacific journal of cancer prevention : APJCP
Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposed system consists of the fo...

A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography.

IEEE transactions on medical imaging
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with a coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to de...

Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.

Medical physics
PURPOSE: Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively e...