119 citations · 256 across the 8 of their papers we have counts for
8 papers
NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
Amirreza Mahbod, Christine Polak, Katharina Feldmann +6
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for…
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images
Amirreza Mahbod, Rahim Entezari, Isabella Ellinger +1
Recently, pruning deep neural networks (DNNs) has received a lot of attention for improving accuracy and generalization power, reducing network size, and increasing inference speed…
FUSeg: The Foot Ulcer Segmentation Challenge
Chuanbo Wang, Amirreza Mahbod, Isabella Ellinger +4
Acute and chronic wounds with varying etiologies burden the healthcare systems economically. The advanced wound care market is estimated to reach $22 billion by 2024. Wound care pr…
Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks
Amirreza Mahbod, Gerald Schaefer, Rupert Ecker +1
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting…
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Amirreza Mahbod, Gerald Schaefer, Benjamin Bancher +4
Nuclei instance segmentation plays an important role in the analysis of Hematoxylin and Eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent t…
Pollen Grain Microscopic Image Classification Using an Ensemble of Fine-Tuned Deep Convolutional Neural Networks
Amirreza Mahbod, Gerald Schaefer, Rupert Ecker +1
Pollen grain micrograph classification has multiple applications in medicine and biology. Automatic pollen grain image classification can alleviate the problems of manual categoris…