1 citations · 1 across the 2 of their papers we have counts for
6 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…
Glioma Classification Using Multimodal Radiology and Histology Data
Azam Hamidinekoo, Tomasz Pieciak, Maryam Afzali +2
Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologi…
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…
Capturing global spatial context for accurate cell classification in skin cancer histology
Konstantinos Zormpas-Petridis, Henrik Failmezger, Ioannis Roxanis +3
The spectacular response observed in clinical trials of immunotherapy in patients with previously uncurable Melanoma, a highly aggressive form of skin cancer, calls for a better un…
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Priya Lakshmi Narayanan, Shan E Ahmed Raza, Andrew Dodson +3
Ki67 is an important biomarker for breast cancer. Classification of positive and negative Ki67 cells in histology slides is a common approach to determine cancer proliferation stat…
Deconvolving convolution neural network for cell detection
Shan E Ahmed Raza, Khalid AbdulJabbar, Mariam Jamal-Hanjani +4
Automatic cell detection in histology images is a challenging task due to varying size, shape and features of cells and stain variations across a large cohort. Conventional deep le…