An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.

Is there an advantage to be gained from adding digital image cytometry of brush cytology to a standard biopsy protocol in patients with Barrett's esophagus?

BACKGROUND AND STUDY AIMS: The current gold standard in Barrett's esophagus monitoring consists of four-quadrant biopsies every 1-2 cm in accordance with the Seattle protocol. Adding brush cytology processed by digital image cytometry (DICM) may further increase the detection of patients with Barrett's esophagus who are at risk of neoplasia. The aim of the present study was to assess the additional diagnostic value and accuracy of DICM when added to the standard histological analysis in a cross-sectional multicenter study of patients with Barrett's esophagus in Switzerland. METHODS: One hundred sixty-four patients with Barrett's esophagus underwent 239 endoscopies with biopsy and brush cytology. DICM was carried out on 239 cytology specimens. Measures of the test accuracy of DICM (relative risk, sensitivity, specificity, likelihood ratios) were obtained by dichotomizing the histopathology results (high-grade dysplasia or adenocarcinoma vs. all others) and DICM results (aneuploidy/intermediate pattern vs. diploidy). RESULTS: DICM revealed diploidy in 83% of 239 endoscopies, an intermediate pattern in 8.8%, and aneuploidy in 8.4%. An intermediate DICM result carried a relative risk (RR) of 12 and aneuploidy a RR of 27 for high-grade dysplasia/adenocarcinoma. Adding DICM to the standard biopsy protocol, a pathological cytometry result (aneuploid or intermediate) was found in 25 of 239 endoscopies (11%; 18 patients) with low-risk histology (no high-grade dysplasia or adenocarcinoma). During follow-up of 14 of these 18 patients, histological deterioration was seen in 3 (21%). CONCLUSION: DICM from brush cytology may add important information to a standard biopsy protocol by identifying a subgroup of BE-patients with high-risk cellular abnormalities.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Towards a user-friendly webservice architecture for statistical machine translation in the PANACEA project

This paper presents a webservice architecture for Statistical Machine Translation aimed at non-technical users. A workfloweditor allows a user to combine different webservices using a graphical user interface. In the current state of this project,the webservices have been implemented for a range of sentential and sub-sententialaligners. The advantage of a common interface and a common data format allows the user to build workflows exchanging different aligners.

Interobserver and intraobserver variability of the apparent diffusion coefficient in treated malignant hepatic lesions on a 3.0T machine: measurements in the whole lesion versus in the area with the most restricted diffusion.

PURPOSE: To assess the inter/intraobserver variability of apparent diffusion coefficient (ADC) measurements in treated hepatic lesions and to compare ADC measurements in the whole lesion and in the area with the most restricted diffusion (MRDA). MATERIALS AND METHODS: Twenty-five patients with treated malignant liver lesions were examined on a 3.0T machine. After agreeing on the best ADC image, two readers independently measured the ADC values in the whole lesion and in the MRDA. These measurements were repeated 1 month later. The Bland-Altman method, Spearman correlation coefficients, and the Wilcoxon signed-rank test were used to evaluate the measurements. RESULTS: Interobserver variability for ADC measurements in the whole lesion and in the MRDA was 0.17 x 10(-3) mm(2)/s [-0.17, +0.17] and 0.43 x 10(-3) mm(2)/s [-0.45, +0.41], respectively. Intraobserver limits of agreement could be as low as [-0.10, +0.12] 10(-3) mm(2)/s and [-0.20, +0.33] 10(-3) mm(2)/s for measurements in the whole lesion and in the MRDA, respectively. CONCLUSION: A limited variability in ADC measurements does exist, and it should be considered when interpreting ADC values of hepatic malignancies. This is especially true for the measurements of the minimal ADC.