[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
Federated learning and differential privacy for medical image analysis
The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification
S Graham, M Jahanifar, A Azam… - Proceedings of the …, 2021 - openaccess.thecvf.com
The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
Weakly supervised deep learning for whole slide lung cancer image analysis
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images
N Hashimoto, D Fukushima, R Koga… - Proceedings of the …, 2020 - openaccess.thecvf.com
We propose a new method for cancer subtype classification from histopathological images,
which can automatically detect tumor-specific features in a given whole slide image (WSI) …
which can automatically detect tumor-specific features in a given whole slide image (WSI) …
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 …
Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland
formation within histology images. To do this, it is important to consider the overall tissue …
formation within histology images. To do this, it is important to consider the overall tissue …
Representation learning of histopathology images using graph neural networks
Abstract Representation learning for Whole Slide Images (WSIs) is pivotal in developing
image-based systems to achieve higher precision in diagnostic pathology. We propose a …
image-based systems to achieve higher precision in diagnostic pathology. We propose a …
Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
H Sun, X Zeng, T Xu, G Peng… - IEEE journal of biomedical …, 2019 - ieeexplore.ieee.org
Uterine cancer (also known as endometrial cancer) can seriously affect the female
reproductive system, and histopathological image analysis is the gold standard for …
reproductive system, and histopathological image analysis is the gold standard for …