Domain adaptation for medical image analysis: a survey
Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …
from the domain shift problem caused by different distributions between source/reference …
[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
Image analysis and machine learning in digital pathology: Challenges and opportunities
A Madabhushi, G Lee - Medical image analysis, 2016 - Elsevier
With the rise in whole slide scanner technology, large numbers of tissue slides are being
scanned and represented and archived digitally. While digital pathology has substantial …
scanned and represented and archived digitally. While digital pathology has substantial …
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In recent years, colorectal cancer has become one of the most significant diseases that
endanger human health. Deep learning methods are increasingly important for the …
endanger human health. Deep learning methods are increasingly important for the …
[HTML][HTML] Multi-class texture analysis in colorectal cancer histology
Automatic recognition of different tissue types in histological images is an essential part in
the digital pathology toolbox. Texture analysis is commonly used to address this problem; …
the digital pathology toolbox. Texture analysis is commonly used to address this problem; …
A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
J Xu, X Luo, G Wang, H Gilmore, A Madabhushi - Neurocomputing, 2016 - Elsevier
Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated
segmentation or classification of EP and ST tissues is important when developing …
segmentation or classification of EP and ST tissues is important when developing …
Breast cancer histopathology image analysis: A review
This paper presents an overview of methods that have been proposed for the analysis of
breast cancer histopathology images. This research area has become particularly relevant …
breast cancer histopathology images. This research area has become particularly relevant …
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides
During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct
histological tumor growth patterns. The percentage of each pattern on multiple slides bears …
histological tumor growth patterns. The percentage of each pattern on multiple slides bears …
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
T Qaiser, YW Tsang, D Taniyama, N Sakamoto… - Medical image …, 2019 - Elsevier
Tumor segmentation in whole-slide images of histology slides is an important step towards
computer-assisted diagnosis. In this work, we propose a tumor segmentation framework …
computer-assisted diagnosis. In this work, we propose a tumor segmentation framework …