End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.

Brain tumors are a threat to life for every other human being, be it adults or children. Gliomas are one of the deadliest brain tumors with an extremely difficult diagnosis. The reason is their complex and heterogenous structure which gives rise to subjective as well as objective errors.

iVision MRSSD: A comprehensive multi-resolution SAR ship detection dataset for state of the art satellite based maritime surveillance applications.

This article describes a comprehensive Synthetic Aperture Radar (SAR) satellite based ships dataset for use in state of the art object detection algorithms. The dataset comprises 11,590 image tiles containing 27,885 ships examples. Each image tile has spatial dimensions of 512 × 512 pixels and is exported in JPEG format.

iVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis.

This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478-901 nm.

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.

During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its ability of handling huge amount of data. Its potential and ability have also been applied and tested in the detection of brain tumor using MRI images for effective prognosis and has shown remarkable performance.

Deep Learning: A Breakthrough in Medical Imaging.

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.

Importance of microscopic stool examination in patients with diarrhoea.

Background: Loose motion is a common symptom in patients reporting to our hospital. As it is a small set up where only facility for microscopic stool examination is available, we designed this study to know how much microscopic stool examination can help us in management of patients with diarrhoea.

Methods: This cross-sectional descriptive study was conducted from January 2010 to April 2012, at Thall Scout Hospital, Hangoo, Khyber Pukhtoon Khwa, Pakistan.