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 …
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 …
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 …
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 …
A dense multi-path decoder for tissue segmentation in histopathology images
Abstract Background and Objective Segmenting different tissue components in
histopathological images is of great importance for analyzing tissues and tumor …
histopathological images is of great importance for analyzing tissues and tumor …
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 …
FEEDNet: A feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis
G Deshmukh, O Susladkar, D Makwana… - Physics in Medicine & …, 2022 - iopscience.iop.org
Objective. Automated cell nuclei segmentation is vital for the histopathological diagnosis of
cancer. However, nuclei segmentation from'hematoxylin and eosin'(HE) stained'whole slide …
cancer. However, nuclei segmentation from'hematoxylin and eosin'(HE) stained'whole slide …
Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships
N Hatipoglu, G Bilgin - Medical & biological engineering & computing, 2017 - Springer
In many computerized methods for cell detection, segmentation, and classification in digital
histopathology that have recently emerged, the task of cell segmentation remains a chief …
histopathology that have recently emerged, the task of cell segmentation remains a chief …
FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images
X Huang, J Chen, M Chen, Y Wan, L Chen - Biocybernetics and Biomedical …, 2023 - Elsevier
Accurate nuclei segmentation is a critical step for physicians to achieve essential information
about a patient's disease through digital pathology images, enabling an effective diagnosis …
about a patient's disease through digital pathology images, enabling an effective diagnosis …
Gsn-hvnet: A lightweight, multi-task deep learning framework for nuclei segmentation and classification
Nuclei segmentation and classification are two basic and essential tasks in computer-aided
diagnosis of digital pathology images, and those deep-learning-based methods have …
diagnosis of digital pathology images, and those deep-learning-based methods have …
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