Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

Purpose: Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans.

Methods: The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection.

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach.

The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices.

Hypothesis testing in noninferiority and equivalence MRMC ROC studies.

Rationale And Objectives: Conventional multireader multicase receiver operating characteristic (MRMC ROC) methodologies use hypothesis testing to test differences in diagnostic accuracies among several imaging modalities. The general MRMC-ROC analysis framework is designed to show that one modality is statistically different among a set of competing modalities (ie, the superiority setting). In practice, one may wish to show that the diagnostic accuracy of a modality is noninferior or equivalent, in a statistical sense, to that of another modality instead of showing its superiority (a higher bar).

Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds.

A previous study [J. Opt. Soc.

An observer study comparing spot imaging regions selected by radiologists and a computer for an automated stereo spot mammography technique.

We are developing an automated stereo spot mammography technique for improved imaging of suspicious dense regions within digital mammograms. The technique entails the acquisition of a full-field digital mammogram, automated detection of a suspicious dense region within that mammogram by a computer aided detection (CAD) program, and acquisition of a stereo pair of images with automated collimation to the suspicious region. The latter stereo spot image is obtained within seconds of the original full-field mammogram, without releasing the compression paddle.