ABSTRACT
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
ABSTRACT
Background Chikungunya fever (CHIKF) is an infectious illness spread by the bite of mosquitoes and caused by an arbovirus known as Chikungunya virus (CHIKV). This disease has become an epidemic in Karachi and various other cities of Pakistan, affecting a large population, especially those from poor, socioeconomically underdeveloped areas. It is proving to be a severe and alarming cause of debility due to its prolonged detrimental effects on the joints. A significant number of cases are reported daily in different hospitals of Karachi, with Civil Hospital being one of the major tertiary care hospitals. The aim of this study is to determine the frequency and symptoms of chikungunya as well as to assess the participants' awareness about the spread and preventive measures of this disease. Methods This is a cross-sectional study that was carried out in Civil Hospital Karachi by approaching patients in the out-patient department (OPD) and the emergency department with complaints of fever and joint pain. All the data was collected via a pre-coded questionnaire during May-June 2017 by taking prior informed verbal consent and were analyzed through Statistical Package for the Social Sciences (SPSS) version 22 software. Results The age group most affected by this disease was the 21-30 years range, which represented almost a quarter of the cases (n=83, 32.17%). Majority of the respondents (n=214, 82.95%) had heard of the disease, mainly due to their own prior experience with it (n=100, 38.76%). Lethargy (n=219, 84.88%), difficulty in walking (n=213, 82.56%), and headache (n=209, 81.01%) were the major symptoms reported apart from fever (n=258, 100.00%) and arthralgia (n=258, 100.00%). Conclusion CHIKF is proving to be a great threat to people as it impairs their quality of life to a great extent. The recent outbreak of chikungunya has victimized a considerable population of Karachi. This study mainly assessed the severity of the disease and its symptoms as well as the lack of awareness among patients. Proper and effective preventive measures can further help to eradicate this disease on a large scale and prevent future epidemics.
