7 citations · 7 across the 5 of their papers we have counts for
7 papers
Cell abundance aware deep learning for cell detection on highly imbalanced pathological data
Yeman Brhane Hagos, Catherine SY Lecat, Dominic Patel +5
Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital patho…
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images
Yeman Brhane Hagos, Priya Lakshmi Narayanan, Ayse U. Akarca +2
In digital pathology, cell detection and classification are often prerequisites to quantify cell abundance and explore tissue spatial heterogeneity. However, these tasks are partic…
Improving Breast Cancer Detection using Symmetry Information with Deep Learning
Yeman Brhane Hagos, Albert Gubern Merida, Jonas Teuwen
Convolutional Neural Networks (CNN) have had a huge success in many areas of computer vision and medical image analysis. However, there is still an immense potential for performanc…
Fast PET Scan Tumor Segmentation using Superpixels, Principal Component Analysis and K-means Clustering
Yeman B. Hagos, Vu H. Minh, Saed Khawaldeh +2
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelit…
A Universal Simulation Platform for Flexible Systems
Vu Hoang Minh, Tajwar Abrar Aleef, Usama Pervaiz +2
This article proposes a universal simulation platform for simulating systems undergoing duress. In other words, this paper introduces a total simulation package which includes a nu…
Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation
Vu Hoang Minh, Tajwar Abrar Aleef, Usama Pervaiz +2
In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals…