Domain-Specific Cues for the Usability of Marker-Controlled Watershed Algorithm and U-Net for Medical Image Segmentation
K Roy, D Bhattacharjee, M Khatun, A Dutta - Artificial Intelligence on …, 2022 - Springer
In medical image analysis, segmentation of the region-of-interest is the crucial phase for
proper diagnosis. However, this task is very challenging due to missing or diffuse …
proper diagnosis. However, this task is very challenging due to missing or diffuse …
Importance of deep learning models to perform segmentation on medical imaging modalities
In the area of medical imaging, segmentation method is vital method to outline the area of
interest in terms of pixels in 2D images or voxels in 3D images. Researchers are focusing on …
interest in terms of pixels in 2D images or voxels in 3D images. Researchers are focusing on …
Improved deep convolutional neural networks (dcnn) approaches for computer vision and bio-medical imaging
MZ Alom - 2018 - rave.ohiolink.edu
Deep learning is showing tremendous success in variety of application domains and
demonstrates state-of-the-art performance over traditional machine learning approaches in …
demonstrates state-of-the-art performance over traditional machine learning approaches in …
An accurate nuclei segmentation algorithm in pathological image based on deep semantic network
Cell (nuclei) segmentation is the basic and key step of pathological image analysis.
However, robust and accurate cell (nuclei) segmentation is a difficult problem due to the …
However, robust and accurate cell (nuclei) segmentation is a difficult problem due to the …
Hybrid convolution neural network in classification of cancer in histopathology images
SP Angayarkanni - Journal of Digital Imaging, 2022 - Springer
Cancer statistics in 2020 reveals that breast cancer is the most common form of cancer
among women in India. One in 28 women is likely to develop breast cancer during their …
among women in India. One in 28 women is likely to develop breast cancer during their …
A deep learning model for automated segmentation of fluorescence cell images
Deep learning techniques bring together key advantages in biomedical image
segmentation. They speed up the process, increase the reproducibility, and reduce the …
segmentation. They speed up the process, increase the reproducibility, and reduce the …
RIC-Unet: An improved neural network based on Unet for nuclei segmentation in histology images
Z Zeng, W Xie, Y Zhang, Y Lu - Ieee Access, 2019 - ieeexplore.ieee.org
As a prerequisite for cell detection, cell classification, and cancer grading, nuclei
segmentation in histology images has attracted wide attention in recent years. It is quite a …
segmentation in histology images has attracted wide attention in recent years. It is quite a …
Cell nuclei segmentation in cryonuseg dataset using nested unet with efficientnet encoder
Cell Nuclei Segmentation is one of the important stages in clinical research. Many
applications in medical treatment, drug discoveries, and disease diagnosis can be benefited …
applications in medical treatment, drug discoveries, and disease diagnosis can be benefited …
Biomedical image segmentation by deep learning methods
KA Davamani, CRR Robin, S Amudha… - … Analysis and Deep …, 2021 - Wiley Online Library
Deep learning methods have been employed to predict and analyse various application in
medical imaging. Deep Learning technology is a computational algorithm that learns by …
medical imaging. Deep Learning technology is a computational algorithm that learns by …
[PDF][PDF] Nuclei segmentation in cell images using fully convolutional neural networks
SS Rautaray, S Dey, M Pandey… - International Journal on …, 2020 - academia.edu
Nuclei detection in microscopy images is a major bottleneck in the discovery of new and
effective drugs. Researchers need to test thousands of chemical compounds to find …
effective drugs. Researchers need to test thousands of chemical compounds to find …