7 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans
Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre +13
Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subje…
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations
Piotr Kaniewski, Fariba Yousefi, Yeman Brhane Hagos +2
In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learnin…
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…