A survey on recent trends in deep learning for nucleus segmentation from histopathology images
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
Application of deep learning in histopathology images of breast cancer: a review
With the development of artificial intelligence technology and computer hardware functions,
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
in histology images is challenging in variable conditions (clinical wild), such as poor staining …
Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images
AA Aatresh, RP Yatgiri, AK Chanchal, A Kumar… - … Medical Imaging and …, 2021 - Elsevier
Image segmentation remains to be one of the most vital tasks in the area of computer vision
and more so in the case of medical image processing. Image segmentation quality is the …
and more so in the case of medical image processing. Image segmentation quality is the …
Sharp dense U-Net: an enhanced dense U-Net architecture for nucleus segmentation
Deep Learning-based algorithms have shown that they are the best at segmenting,
processing, detecting, and classifying medical images. U-Net is a famous Deep Learning …
processing, detecting, and classifying medical images. U-Net is a famous Deep Learning …
Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images
To improve the process of diagnosis and treatment of cancer disease, automatic
segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology …
segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology …
High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images
Purpose Increasing cancer disease incidence worldwide has become a major public health
issue. Manual histopathological analysis is a common diagnostic method for cancer …
issue. Manual histopathological analysis is a common diagnostic method for cancer …
A multi-scale 3-stacked-layer coned U-net framework for tumor segmentation in whole slide images
The contribution of deep learning in medical image diagnosis has gained extensive interest
due to its excellent performance. Furthermore, the interest has also grown in digital …
due to its excellent performance. Furthermore, the interest has also grown in digital …
Evolution of LiverNet 2. x: Architectures for automated liver cancer grade classification from H&E stained liver histopathological images
AK Chanchal, S Lal, D Barnwal, P Sinha… - Multimedia Tools and …, 2024 - Springer
Recently, the automation of disease identification has been quite popular in the field of
medical diagnosis. The rise of Convolutional Neural Networks (CNNs) for training and …
medical diagnosis. The rise of Convolutional Neural Networks (CNNs) for training and …
RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning
Z Wu, X Li, J Zuo - Frontiers in Oncology, 2023 - frontiersin.org
Objective Due to the small proportion of target pixels in computed tomography (CT) images
and the high similarity with the environment, convolutional neural network-based semantic …
and the high similarity with the environment, convolutional neural network-based semantic …