Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions

Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassing multi-scalar features acquired at different levels of the network. This helps segment target regions (lesions) of varying shape, size and contrast. Incorporation of the entire gamut of multi-scalar characteristics into the novel attention mechanism helps prioritize the selection of activation responses and locations containing useful information. Determining robust and discriminatory features along the decoder path is facilitated with deep supervision. Connections in the decoder arm are remodeled to handle the issue of vanishing gradient. As observed from the experimental results, FuDSA-Net surpasses other state-of-the-art architectures; especially, when it comes to characterizing complicated geometries of the lesions

A survey and classification of publicly available COVID-19 datasets

The current study curates a list of authentic and open-access sources of alphanumeric COVID-19 pandemic data. We have gathered 74 datasets from 42 sources, including sources from 18 countries. The datasets are searched through the Kaggle and GitHub repositories besides Google, providing a representation of varieties of pandemic-related datasets. The datasets are categorized according to their sources- primary and secondary, and according to their geographical distribution. While analyzing the dataset, we came across some classes in which the datasets can be categorized. We present the categorization in the form of taxonomy and highlight the present COVID-19 data collection and use challenges. The study will help researchers and data curators in the identification and classification of pandemic data

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Feasability of Open Schooling in Disturbed Areas: A Case Study of Afghanistan

Most countries have enshrined right to education in their constitution but in reality to fulfil this commitment countries do face a number of challenges. And this is true with the Islamic Republic of Afghanistan, which unlike other countries has a long history of war, conflicts, insurgency and hence insecurity. Although there have been positive steps towards rehabilitation of the education system and signs of promises can be seen in its achievements, access to quality education remains inequitable particularly across the provinces as a result of remoteness and geographical isolation, harsh climate, insecurity which impedes growth and sustainability of access points, high gender gap in all sectors of education particularly from lower secondary stage to higher stages of education, poor infrastructure prevalent in most schools, untrained teachers and low number of female teachers affecting participation, retention and continuity of studies.This paper highlights the current school educational status in Afghanistan to reveal the daunting challenges still existing to confront for the country to achieve its constitutional goals. It will also points out how Open schooling system can take charge of the challenges in Afghanistan to provide a channel of educational opportunities to those who cannot and do not go to school particularly the girls and women.  (Note:This article was orginally presented in: The International Conference on Education for All: Role of Open Schooling, 13th -15th March 2013, New Delhi