AIMC Topic: Image Processing, Computer-Assisted

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Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, des...

Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment...

Multicontext multitask learning networks for mass detection in mammogram.

Medical physics
PURPOSE: In this paper, for the purpose of accurate and efficient mass detection, we propose a new deep learning framework, including two major stages: Suspicious region localization (SRL) and Multicontext Multitask Learning (MCMTL).

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb.

Scientific reports
The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the medical staff spend several hours observing whether the CE enters the descending segment of the duodenu...

Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population.

PloS one
Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature. Abnormalities of the retinal vasculature may reflect the degree of microvascular damage due to hype...

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

PloS one
Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant ...

NFN+: A novel network followed network for retinal vessel segmentation.

Neural networks : the official journal of the International Neural Network Society
In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play an essential role to construct a retinal computer-aided diagnosis system. However, due to the challenges including limited densely annotated data, inte...

Artificial intelligence as the next step towards precision pathology.

Journal of internal medicine
Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to i...

SOMprocessor: A high throughput FPGA-based architecture for implementing Self-Organizing Maps and its application to video processing.

Neural networks : the official journal of the International Neural Network Society
The design of neuromorphic chips aims to develop electronic circuits dedicated to executing artificial neural networks, mainly by exploring parallel processing. Unsupervised learning models, such as Self-organizing Maps (SOM), may benefit from massiv...

Image segmentation based on gray level and local relative entropy two dimensional histogram.

PloS one
Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image's brightness information is taken into account in the procedure of threshold selection. Considering the contextual infor...