Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
A review on 2D instance segmentation based on deep neural networks
W Gu, S Bai, L Kong - Image and Vision Computing, 2022 - Elsevier
Image instance segmentation involves labeling pixels of images with classes and instances,
which is one of the pivotal technologies in many domains, such as natural scenes …
which is one of the pivotal technologies in many domains, such as natural scenes …
[HTML][HTML] On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
[HTML][HTML] In silico labeling: predicting fluorescent labels in unlabeled images
Microscopy is a central method in life sciences. Many popular methods, such as antibody
labeling, are used to add physical fluorescent labels to specific cellular constituents …
labeling, are used to add physical fluorescent labels to specific cellular constituents …
Suggestive annotation: A deep active learning framework for biomedical image segmentation
Image segmentation is a fundamental problem in biomedical image analysis. Recent
advances in deep learning have achieved promising results on many biomedical image …
advances in deep learning have achieved promising results on many biomedical image …
Deep adversarial networks for biomedical image segmentation utilizing unannotated images
Semantic segmentation is a fundamental problem in biomedical image analysis. In
biomedical practice, it is often the case that only limited annotated data are available for …
biomedical practice, it is often the case that only limited annotated data are available for …
A deep learning framework for supporting the classification of breast lesions in ultrasound images
A deep learning framework for supporting the classification of breast lesions in ultrasound
images - IOPscience Skip to content IOP Science home Accessibility Help Search Journals …
images - IOPscience Skip to content IOP Science home Accessibility Help Search Journals …
MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images
The analysis of glandular morphology within colon histopathology images is an important
step in determining the grade of colon cancer. Despite the importance of this task, manual …
step in determining the grade of colon cancer. Despite the importance of this task, manual …
Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
K Men, J Dai, Y Li - Medical physics, 2017 - Wiley Online Library
Purpose Delineation of the clinical target volume (CTV) and organs at risk (OAR s) is very
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …