Nuclei segmentation in colon histology images by using the deep CNNs: a U-net based multi-class segmentation analysis
As is known, pathologists visually examine the tissue distributions by using microscopes
traditionally. The rise in digital image processing and machine learning also allows high …
traditionally. The rise in digital image processing and machine learning also allows high …
Combining Deep-Learned and Hand-Crafted Features for Segmentation, Classification and Counting of Colon Nuclei in H &E Stained Histology Images
P Dumbhare, Y Dubey, V Phuse, A Jamthikar… - … on Computer Vision and …, 2022 - Springer
Colon nuclei detection within Haematoxylin & Eosin (H &E) stained histology images is
important to mitigate abnormalities or diseases like colon cancer in its early stages …
important to mitigate abnormalities or diseases like colon cancer in its early stages …
ciscNet--A Single-Branch Cell Instance Segmentation and Classification Network
Automated cell nucleus segmentation and classification are required to assist pathologists in
their decision making. The Colon Nuclei Identification and Counting Challenge 2022 …
their decision making. The Colon Nuclei Identification and Counting Challenge 2022 …
[PDF][PDF] Neural network based segmentation of cell nuclei and lymphocyte detection in whole slide histology images
E Budginaitė - Lietuvos magistrantų informatikos ir IT tyrimai, 2019 - zurnalai.vu.lt
Visual examination of cancer tissue is regarded as the golden standard diagnostic method
for experienced pathologist. Assessment of cell morphology and tissue distribution are the …
for experienced pathologist. Assessment of cell morphology and tissue distribution are the …
An automatic cell nuclei segmentation based on deep learning strategies
A Mandloi, U Daripa, M Sharma… - 2019 IEEE Conference …, 2019 - ieeexplore.ieee.org
Automatic analysis of histopathology specimens images can be utilized in early extraction
and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early …
and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early …
[PDF][PDF] Deep learning algorithms for convolutional neural networks (cnns) using an appropriate cell-segmentation method
J Alshudukhi - J. Comput. Sci. Eng. Inf. Technol. Res, 2022 - academia.edu
Cancer is one of the most common and deadly diseases in the world, accounting for a
significant number of fatalities each year. For this condition, early detection and …
significant number of fatalities each year. For this condition, early detection and …
An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images
Background Since nuclei segmentation in histopathology images can provide key
information for identifying the presence or stage of a disease, the images need to be …
information for identifying the presence or stage of a disease, the images need to be …
Transfer learning approach and nucleus segmentation with medclnet colon cancer database
Abstract Machine learning has been recently used especially in the medical field. In the
diagnosis of serious diseases such as cancer, deep learning techniques can be used to …
diagnosis of serious diseases such as cancer, deep learning techniques can be used to …
Hybrid Deep Learning Architectures for Histological Image Segmentation
MJ Hasan, WSHMW Ahmad… - … in Information and …, 2024 - ieeexplore.ieee.org
In histopathology image analysis, accurate segmentation of nuclei holds immense
significance, particularly in the early detection and treatment of diseases like breast cancer …
significance, particularly in the early detection and treatment of diseases like breast cancer …
Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images
Image segmentation is consistently an important task for computer vision and the analysis of
medical images. The analysis and diagnosis of histopathology images by using efficient …
medical images. The analysis and diagnosis of histopathology images by using efficient …