HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. Digitize tissue slide images are used to analyze and segment glands, nuclei, and other biomarkers which are further used in computer-aided medical applications. To this end, many researchers developed different neural networks to perform segmentation on histological images, mostly these networks are based on encoder-decoder architecture and also utilize complex attention modules or transformers. However, these networks are less accurate to capture relevant local and global features with accurate boundary detection at multiple scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE) Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with 1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS dataset. Implementation Code is available at this link: https://bit.ly/HistoSegComment: Accepted by 2022 12th International Conference on Pattern Recognition Systems (ICPRS), For Implementation Code see https://bit.ly/HistoSe

Designing an IT-Based System for Optimizing Lung Cancer Management

Digital health offers lung cancer patients to improve their health status while allowing patients, providers, and administrators to coordinate data and care at individual and community levels. While technology improvements provide lung cancer patients and healthcare providers with a valuable new tool for disease management, these are yet to be widely accepted. In particular, we aim to: (1) develop a Machine Learning (ML)-based framework for data collection from active online lung cancer forums and other parameters for patients, providers and their organizations, (2) build an AI-based model to develop a cancer ontology for exploring different factors and patients’ emotions associated to lung cancer management, (3) Design a mHealth app to set up a support system in terms of providing patients with information and social support, and ML models-based treatment recommendation system. The IT-based support system will provide the best and most specific treatment plan and recommendation system for lung cancer management

HPC as a Service: A naive model

Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is out of range from the majority. Nowadays Supercomputers are actually clusters of computers usually made-up of commodity hardware. Such clusters are called Beowulf Clusters. The history of which goes back to 1994 when NASA built a Supercomputer by creating a cluster of commodity hardware. In recent times a lot of effort has been done in making HPC Clusters of even single board computers (SBCs). Although the creation of clusters of commodity hardware is possible but is a cumbersome task. Moreover, the maintenance of such systems is also difficult and requires special expertise and time. The concept of cloud is to provide on-demand resources that can be services, platform or even infrastructure and this is done by sharing a big resource pool. Cloud computing has resolved problems like maintenance of hardware and requirement of having expertise in networking etc. An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud. The main target is to create a system which can develop a capability of providing computing power as a service which to further be referred to as Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B and 3B+ Single Board Computers. The reason for using RPi boards was increasing popularity of ARM processors in the field of HPCComment: 2019 8th International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan, 201

Patterns of Traumatic Brain Injuries in Patients Presenting at a Tertiary Care Unit

Objective: The purpose of this study was to evaluate the distribution of traumatic brain injuries.Patients and Methods: Questionnaire based data was collected from an inpatient population of patients who presented to the Neurosurgical Unit of Ayub Teaching Hospital with traumatic brain injuries. CAT scan (Computerized Automated Tomography scan) was used as the imaging modality for preliminary diagnosis. The data was collected over a period of three years. Non-probability purposive sampling was used as the sampling technique. Patients of both sexes and all ages were included in the study.Results: Out of a total of 1938 patients, 1470 (75.9%) were males and 468 (24.1%) were females. Patients from 20 to 40 years’ age group (38.1%) represented the greatest number. Fall (52.6%) was found to be the most common external cause of Traumatic brain injury, followed by Road Traffic Accidents (34.1%). Most of the patients (42.4%) had a GCS score falling between 8 and 12. No lesion was found at the initial CAT scan in most of the patients (27.2%). Depressed Skull Fracture (21.4%) was the most common abnormal finding in initial imaging. Conservative (78.3%) treatment was provided to most of the patients keeping in view the appropriate management requirements. 97.8% of the patients were treated successfully.Conclusion: The quality of care at Neurosurgery Ayub Teaching Hospital was found to be up to the mark for traumatic brain injuries patients. However, the standard of care at Kashmir and Balakot needs to be re-evaluated

A New Random Walk for Replica Detection in WSNs

The authors wish to thanks the anonymous reviewers for their valuable comments for the improvement of this manuscript. The authors wish to acknowledge the support and help of Deanship of Scientific Research at Jazan University and the authors also extend their sincere appreciations to Deanship of Scientific Research at King Saud University for its funding this Prolific Research Group (PRG-1436-16).Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.Yeshttp://www.plosone.org/static/editorial#pee

Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

<div><p>Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.</p></div