[HTML][HTML] Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
Cell nucleus segmentation in color histopathological imagery using convolutional networks
B Pang, Y Zhang, Q Chen, Z Gao… - 2010 Chinese …, 2010 - ieeexplore.ieee.org
Recent studies have shown that convolutional networks can achieve a great deal of success
in high-level vision problems such as objection recognition. In this paper, convolutional …
in high-level vision problems such as objection recognition. In this paper, convolutional …
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …
Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks
Accurate cell segmentation is essential for computer-aided diagnosis of cervical
precancerous lesions in cytology images. Automated segmentation poses a great challenge …
precancerous lesions in cytology images. Automated segmentation poses a great challenge …
CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron
Accurate segmentation of lesions in medical images is of great significance for clinical
diagnosis and evaluation. The low contrast between lesions and surrounding tissues …
diagnosis and evaluation. The low contrast between lesions and surrounding tissues …
Nuclei and glands instance segmentation in histology images: a narrative review
Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
[HTML][HTML] Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
Background Histopathology image analysis is a gold standard for cancer recognition and
diagnosis. Automatic analysis of histopathology images can help pathologists diagnose …
diagnosis. Automatic analysis of histopathology images can help pathologists diagnose …
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 …
DMCNN: a deep multiscale convolutional neural network model for medical image segmentation
L Teng, H Li, S Karim - Journal of Healthcare Engineering, 2019 - Wiley Online Library
Medical image segmentation is one of the hot issues in the related area of image
processing. Precise segmentation for medical images is a vital guarantee for follow‐up …
processing. Precise segmentation for medical images is a vital guarantee for follow‐up …
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
This paper addresses the task of nuclei segmentation in high-resolution histopathology
images. We propose an automatic end-to-end deep neural network algorithm for …
images. We propose an automatic end-to-end deep neural network algorithm for …