MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information
Accurate segmentation of nuclei in digital pathology images can assist doctors in diagnosing
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …
An optimized multi-organ cancer cells segmentation for histopathological images based on CBAM-residual U-Net
In digital pathology, the accurate segmentation of cell nuclei in histopathology images is
essential for medical image analysis. Histopathologists visually evaluate the patterns of …
essential for medical image analysis. Histopathologists visually evaluate the patterns of …
AlexSegNet: an accurate nuclei segmentation deep learning model in microscopic images for diagnosis of cancer
A Singha, MK Bhowmik - Multimedia tools and Applications, 2023 - Springer
The nuclei segmentation of microscopic images is a key pre-requisite for cancerous
pathological image analysis. However, an accurate nuclei cell segmentation is a long …
pathological image analysis. However, an accurate nuclei cell segmentation is a long …
DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images
Cancer is the second deadliest disease globally that can affect any human body organ.
Early detection of cancer can increase the chances of survival in humans. Morphometric …
Early detection of cancer can increase the chances of survival in humans. Morphometric …
A two-stage U-Net algorithm for segmentation of nuclei in H&E-stained tissues
Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and
eosin (H&E)-stained tissue sections. While various segmentation methods have been …
eosin (H&E)-stained tissue sections. While various segmentation methods have been …
Robust segmentation of nucleus in histopathology images via mask R-CNN
Nuclei segmentation plays an import role in histopathology images analysis. Deep learning
approaches have shown its strength for histopathology images processing in various …
approaches have shown its strength for histopathology images processing in various …
Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation
B Zhao, X Chen, Z Li, Z Yu, S Yao, L Yan, Y Wang… - Medical Image …, 2020 - Elsevier
Nuclei segmentation is a vital step for pathological cancer research. It is still an open
problem due to some difficulties, such as color inconsistency introduced by non-uniform …
problem due to some difficulties, such as color inconsistency introduced by non-uniform …
[HTML][HTML] Pathology image analysis using segmentation deep learning algorithms
With the rapid development of image scanning techniques and visualization software, whole
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis …
Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models
SH Kassani, PH Kassani, MJ Wesolowski… - International Journal of …, 2022 - Elsevier
Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early
detection of suspicious tissues can significantly improve the survival rate. In this study, the …
detection of suspicious tissues can significantly improve the survival rate. In this study, the …