AI Medical Compendium Topic

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Image Enhancement

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Scanning double-sided documents without incurring show-through by learning to fuse two complementary images using multilayer perceptron.

PloS one
This paper presents a novel method for scanning duplex-printed documents without incurring the unwanted show-through artifact. The proposed method achieves the goal of eliminating the leaked-out reverse-side content by fusing a white backed scan imag...

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

BMC medical imaging
BACKGROUND: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit ...

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...

Computer vision-based diameter maps to study fluoroscopic recordings of small intestinal motility from conscious experimental animals.

Neurogastroenterology and motility
BACKGROUND: When available, fluoroscopic recordings are a relatively cheap, non-invasive and technically straightforward way to study gastrointestinal motility. Spatiotemporal maps have been used to characterize motility of intestinal preparations in...

Deep Learning in Medical Image Analysis.

Annual review of biomedical engineering
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the ...

Implementing Machine Learning in Radiology Practice and Research.

AJR. American journal of roentgenology
OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data ...

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

Neural networks : the official journal of the International Neural Network Society
Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performanc...

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.

IEEE transactions on medical imaging
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learnin...