Instance-Aware Feature Alignment for Cross-Domain Cell Nuclei Detection in Histopathology Images

2021 ◽  
pp. 499-508
Author(s):  
Zhi Wang ◽  
Xiaoya Zhu ◽  
Lei Su ◽  
Gang Meng ◽  
Junsheng Zhang ◽  
...  
Keyword(s):  
Cell Nuclei ◽  
Cross Domain ◽  
Nuclei Detection ◽  
Histopathology Images ◽  
Cell Nuclei Detection ◽  
Feature Alignment

An Iterated Local Search-Based Algorithm to Support Cell Nuclei Detection in Pap Smears Test

2020 ◽  
pp. 78-96
Author(s):  
Débora N. Diniz ◽  
Marcone J. F. Souza ◽  
Claudia M. Carneiro ◽  
Daniela M. Ushizima ◽  
Fátima N. S. de Medeiros ◽  
...  
Keyword(s):  
Local Search ◽  
Iterated Local Search ◽  
Pap Smears ◽  
Cell Nuclei ◽  
Support Cell ◽  
Nuclei Detection ◽  
Cell Nuclei Detection

scholarly journals Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images

2019 ◽  
Vol 5 (1) ◽  
pp. 21 ◽  
Author(s):  
Haonan Zhou ◽  
Raju Machupalli ◽  
Mrinal Mandal
Keyword(s):  
Processing Time ◽  
Detection Algorithm ◽  
Fpga Implementation ◽  
Detection Accuracy ◽  
Cell Nuclei ◽  
Nuclei Detection ◽  
Efficient Detection ◽  
Field Programmable ◽  
Histopathology Images ◽  
Aided Diagnosis

Accurate and efficient detection of cell nuclei is an important step towards the development of a pathology-based Computer Aided Diagnosis. Generally, high-resolution histopathology images are very large, in the order of billion pixels, therefore nuclei detection is a highly compute intensive task, and software implementation requires a significant amount of processing time. To assist the doctors in real time, special hardware accelerators, which can reduce the processing time, are required. In this paper, we propose a Field Programmable Gate Array (FPGA) implementation of automated nuclei detection algorithm using generalized Laplacian of Gaussian filters. The experimental results show that the implemented architecture has the potential to provide a significant improvement in processing time without losing detection accuracy.

scholarly journals Local Integral Regression Network for Cell Nuclei Detection

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1336
Author(s):  
Xiao Zhou ◽  
Miao Gu ◽  
Zhen Cheng
Keyword(s):  
Cellular Heterogeneity ◽  
Cell Nuclei ◽  
Data Annotation ◽  
Nuclei Detection ◽  
Local Integral ◽  
Density Map ◽  
Weakly Supervised ◽  
Histopathology Image Analysis ◽  
Training Demands ◽  
Cell Nuclei Detection

Nuclei detection is a fundamental task in the field of histopathology image analysis and remains challenging due to cellular heterogeneity. Recent studies explore convolutional neural networks to either isolate them with sophisticated boundaries (segmentation-based methods) or locate the centroids of the nuclei (counting-based approaches). Although these two methods have demonstrated superior success, their fully supervised training demands considerable and laborious pixel-wise annotations manually labeled by pathology experts. To alleviate such tedious effort and reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both fully and weakly supervised learning (FSL/WSL) frameworks for nuclei detection. Furthermore, the LIRNet can output an exquisite density map of nuclei, in which the localization of each nucleus is barely affected by the post-processing algorithms. The quantitative experimental results demonstrate that the FSL version of the LIRNet achieves a state-of-the-art performance compared to other counterparts. In addition, the WSL version has exhibited a competitive detection performance and an effortless data annotation that requires only 17.5% of the annotation effort.

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