Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-35

Lung Nodule Detection in Screening Computed Tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images with 1.25 mm slice thickness is presented. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The results obtained on the collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the IEEE NNS and MIC Conference, Oct. 29 - Nov. 4, 2006, San Diego, Californi

Diffusion Tensor Imaging and Tractography in Brown-Sequard Syndrome

This report illustrates the utility of DTI and DTT in delineating regions of cord injury in two patients with traumatic Brown-Sequard syndrome. Our results indicate that DTI provides clinically relevant information that supplements conventional MR imaging for patients with acute SCI

GPCALMA: a Grid-based tool for Mammographic Screening

The next generation of High Energy Physics (HEP) experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the Virtual Organisation (VO), a group of distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and proved to be useful in improving the radiologists' performances in the diagnosis. GRID technologies allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated to screening programs. A prototype of the system, based on AliEn GRID Services, is already available, with a central Server running common services and several clients connecting to it. Mammograms can be acquired in any location; the related information required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. The result of a query can be used as input for analysis algorithms, which are executed on nodes that are in general remote to the user (but always local to the input images) thanks to the PROOF facility. The selected approach avoids data transfers for all the images with a negative diagnosis (about 95% of the sample) and allows an almost real time diagnosis for the 5% of images with high cancer probability.Comment: 9 pages, 4 figures; Proceedings of the HealthGrid Workshop 2004, January 29-30, Clermont-Ferrand, Franc

Observation of the thermal Casimir force

Quantum theory predicts the existence of the Casimir force between macroscopic bodies, due to the zero-point energy of electromagnetic field modes around them. This quantum fluctuation-induced force has been experimentally observed for metallic and semiconducting bodies, although the measurements to date have been unable to clearly settle the question of the correct low-frequency form of the dielectric constant dispersion (the Drude model or the plasma model) to be used for calculating the Casimir forces. At finite temperature a thermal Casimir force, due to thermal, rather than quantum, fluctuations of the electromagnetic field, has been theoretically predicted long ago. Here we report the experimental observation of the thermal Casimir force between two gold plates. We measured the attractive force between a flat and a spherical plate for separations between 0.7 $\mu$m and 7 $\mu$m. An electrostatic force caused by potential patches on the plates' surfaces is included in the analysis. The experimental results are in excellent agreement (reduced $\chi^2$ of 1.04) with the Casimir force calculated using the Drude model, including the T=300 K thermal force, which dominates over the quantum fluctuation-induced force at separations greater than 3 $\mu$m. The plasma model result is excluded in the measured separation range.Comment: 6 page

Social Control in Transnational Families: Somali Women and Dignity in Johannesburg

Transnational mobility often separates families and distances individuals from the kinship and social structures by which they organized their lives prior to migration. Myriad forms of insecurity have been the impetus for Somali movement into the diaspora, with people fleeing the realities of conflict that have marked Somalia for decades while physically dividing families as individuals settle in different countries around the world. Mobility has altered the dynamics of households, families, and communities post-migration, reshaping social constructions as individuals move on without the familial support that sustained them in Somalia. While outcomes of these hardships are variable and often uneven in different settlement spaces, migration can offer new opportunities for people to pursue avenues from which they were previously excluded, such as by assuming roles and responsibilities their relatives once filled. These changes precipitate shifting identities and are challenging for women who find themselves self-reliant in the diaspora, particularly in the absence of (supportive) husbands and close kin.Drawing on ethnographic research in Johannesburg’s Somali community, this chapter explores the assumption that migration provides an opening for women to challenge subordinating gender norms. Settlement often grants women greater freedom to make choices in their lives, such as in employment and personal relationships, and yet they remain constrained by networks that limit their autonomy. Even with transnational migration and protracted separation, women are family representatives who must uphold cultural notions of respectability despite realities that position them as guardians and family providers. Women remain under the watchful eye of their extended families through expansive networks and the ease of modern communication, which facilitate a new form of social control as women’s behavior is carefully monitored and reported to relatives afar. These actualities raise questions about the degree to which transnational movement is a liberating force for women or rather a reconfiguration of social control. I argue that despite women’s changing position in their households and families, they remain limited by social control within their extended families and communities

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine