Fuzzy directional enlacement landscapes

International audienceSpatial relations between objects represented in images are of high importance in various application domains related to pattern recognition and computer vision. By definition, most relations are vague, ambiguous and difficult to formalize precisely by humans. The issue of describing complex spatial configurations, where objects can be imbri-cated in each other, is addressed in this article. A novel spatial relation, called enlacement, is presented and designed using a directional fuzzy landscape approach. We propose a generic fuzzy model that allows to visualize and evaluate complex enlacement configurations between crisp objects, with directional granularity. The interest and the behavior of this approach is highlighted on several characteristic examples

Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA

Whistleblowers as regulatory intermediaries: Instrumental and reflexive considerations in decentralizing regulation

This article frames whistleblowers as regulatory intermediaries who provide a response to the problem posed by the fragmentation of knowledge in a complex society and market economy. I identify two ways in which whistleblowers become regulatory intermediaries: The first is by remedying informational asymmetries between the regulator and the target (instrumental approach). Both in the United States and in the European Union, whistleblowers are protected on the basis of the value of the disclosed information for the advancement of regulatory objectives. The second way in which whistleblowers become regulatory intermediaries is by contributing to the development of “communities of compliance” and by enhancing the internal self-regulatory capacities of regulatory targets (reflexive approach). Creating internal channels of reporting and monitoring is perceived as a way to change the organizational culture of targets. Through the instrumentalism – reflexivity dipole, competing rationales and normative visions of regulatory intermediation become apparent: It could, on the one hand, facilitate state intervention and legal sanctions or, on the other hand, signal the aspiration to embed public and social values in private actors

Neurosphere fate prediction: An analysis-synthesis approach for feature extraction

10.1109/IJCNN.2012.6252628Proceedings of the International Joint Conference on Neural Networks85OF

Mitosis detection in breast cancer histological images An ICPR 2012 contest

Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Results : Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Conclusions : Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them

Milena: Write Generic Morphological Algorithms Once, Run on Many Kinds of Images

International audienceWe present a programming framework for discrete mathematical morphology centered on the concept of genericity. We show that formal definitions of morphological algorithms can be translated into actual code, usable on virtually any kind of compatible images, provided a general definition of the concept of image is given. This work is implemented in Milena, a generic, efficient, and user-friendly image processing library