Multi-instance multi-label learning for whole slide breast histopathology

Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis systems. However, the whole slide images also provide two new challenges to image analysis algorithms. The first one is the need for simultaneous localization and classification of malignant areas in these large images, as different parts of the image may have different levels of diagnostic relevance. The second challenge is the uncertainty regarding the correspondence between the particular image areas and the diagnostic labels typically provided by the pathologists at the slide level. In this paper, we exploit a data set that consists of recorded actions of pathologists while they were interpreting whole slide images of breast biopsies to find solutions to these challenges. First, we extract candidate regions of interest (ROI) from the logs of pathologists' image screenings based on different actions corresponding to zoom events, panning motions, and fixations. Then, we model these ROIs using color and texture features. Next, we represent each slide as a bag of instances corresponding to the collection of candidate ROIs and a set of slide-level labels extracted from the forms that the pathologists filled out according to what they saw during their screenings. Finally, we build classifiers using five different multi-instance multi-label learning algorithms, and evaluate their performances under different learning and validation scenarios involving various combinations of data from three expert pathologists. Experiments that compared the slide-level predictions of the classifiers with the reference data showed average precision values up to 62% when the training and validation data came from the same individual pathologist's viewing logs, and an average precision of 64% was obtained when the candidate ROIs and the labels from all pathologists were combined for each slide. © 2016 SPIE

VACCINATION PRACTICES OF ADULT FAMILIAL MEDITERRANEAN FEVER PATIENTS IN TURKEY.

[Abstract Not Available

Free-radical scavenging capacity and antimicrobial activity of wild edible mushroom from Turkey

Antioxidant capacity and antimicrobial activities of Ramaria flava (Schaeff) Quel. (RF) extracts obtained with ethanol were investigated in this study. Four complementary test systems; namely DPPH freeradical scavenging, -carotene/linoleic acid systems, total phenolic compounds and total flavonoid concentration have been used. Inhibition values of R. flava extracts, BHA and -tocopherol standardswere found to be 94.7, 98.9 and 99.2%, respectively, at 160ƒÊg/ml. When compared the inhibition levels of ethanol extract of R. flava and standards in linoleic acid system, it was observed that the higher theconcentration of both RF ethanol extract and the standards the higher the inhibition effect. Total flavonoid amount was 8.27}0.28 ƒÊg mg-1 quercetin equivalent while the total phenolic compound amountwas 39.83}0.32 ƒÊg mg-1 pyrocatechol equivalent in the ethanolic extract. The ethanol extract of R. flava inhibited the growth of Gram-positive bacteria better than Gram-negative bacteria and yeast. The crude extract showed no antibacterial activity against Pseudomonas aeruginosa, Escherichia coli, Morganella morganii and Proteus vulgaris. The antimicrobial activity profile of R. flava against tested strains indicated that Micrococcus flavus, Micrococcus luteus and Yersinia enterocolitica was the most susceptible bacteria of all the test strains. R. flava was found to be inactive against Candida albicans

EPIDEMIOLOGICAL CHARACTERISTICS OF VIRAL HEPATITIS IN PATIENTS WITH RHEUMATIC DISEASES - IMPLICATIONS FROM TREASURE DATABASE

EULAR European Congress of Rheumatology (EULAR) -- JUN 01-04, 2022 -- Copenhagen, DENMARK[Abstract Not Available]European Alliance Assoc Rheumato

PARADOXICAL REACTIONS, ESPECIALLY PSORIASIS IN RHEUMATOLOGY PATIENTS RECEIVING BIOLOGIC THERAPY FROM THE TREASURE DATABASE: A 5-YEAR FOLLOW-UP STUDY

EULAR European Congress of Rheumatology (EULAR) -- JUN 01-04, 2022 -- Copenhagen, DENMARK[Abstract Not Available]European Alliance Assoc Rheumato

BIOLOGIC DRUG PREFERENCES OF TURKISH RHEUMATOLOGISTS IN SPONDILOARTROPATHY PATIENTS WITH ADVANCED CHRONIC RENAL DISEASE

[Abstract Not Available

THE DIFFERENCES BETWEEN THE FIRST PREFERRED BIOLOGICAL DMARD AND THE DRUG SURVIVAL IN GERIATRIC AND YOUNGER ADULT POPULATION WITH RHEUMATOID ARTHRITIS AND PSORIATIC ARTHRITIS: TREASURE REAL-LIFE DATA

EULAR European Congress of Rheumatology (EULAR) -- JUN 01-04, 2022 -- Copenhagen, DENMARK[Abstract Not Available]European Alliance Assoc Rheumato

Angiogenic Peptide Nanofibers Improve Wound Healing in STZ-Induced Diabetic Rats

Low expressions of angiogenic growth factors delay the healing of diabetic wounds by interfering with the process of blood vessel formation. Heparin mimetic peptide nanofibers can bind to and enhance production and activity of major angiogenic growth factors, including VEGF. In this study, we showed that heparin mimetic peptide nanofibers can serve as angiogenic scaffolds that allow slow release of growth factors and protect them from degradation, providing a new therapeutic way to accelerate healing of diabetic wounds. We treated wounds in STZ-induced diabetic rats with heparin mimetic peptide nanofibers and studied repair of full-thickness diabetic skin wounds. Wound recovery was quantified by analyses of re-epithelialization, granulation tissue formation and blood vessel density, as well as VEGF and inflammatory response measurements. Wound closure and granulation tissue formation were found to be significantly accelerated in heparin mimetic gel treated groups. In addition, blood vessel counts and the expressions of alpha smooth muscle actin and VEGF were significantly higher in bioactive gel treated animals. These results strongly suggest that angiogenic heparin mimetic nanofiber therapy can be used to support the impaired healing process in diabetic wounds. © 2016 American Chemical Society

Localization of diagnostically relevant regions of interest in whole slide images

Whole slide imaging technology enables pathologists to screen biopsy images and make a diagnosis in a digital form. This creates an opportunity to understand the screening patterns of expert pathologists and extract the patterns that lead to accurate and efficient diagnoses. For this purpose, we are taking the first step to interpret the recorded actions of world-class expert pathologists on a set of digitized breast biopsy images. We propose an algorithm to extract regions of interest from the logs of image screenings using zoom levels, time and the magnitude of panning motion. Using diagnostically relevant regions marked by experts, we use the visual bag-of-words model with texture and color features to describe these regions and train probabilistic classifiers to predict similar regions of interest in new whole slide images. The proposed algorithm gives promising results for detecting diagnostically relevant regions. We hope this attempt to predict the regions that attract pathologists' attention will provide the first step in a more comprehensive study to understand the diagnostic patterns in histopathology. © 2014 IEEE

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine