2,146 research outputs found
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding
Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address the classification problem. Due to the large size of each
image in the training dataset, we propose a patch-based technique which
consists of two consecutive convolutional neural networks. The first
"patch-wise" network acts as an auto-encoder that extracts the most salient
features of image patches while the second "image-wise" network performs
classification of the whole image. The first network is pre-trained and aimed
at extracting local information while the second network obtains global
information of an input image. We trained the networks using the ICIAR 2018
grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method
yields 95 % accuracy on the validation set compared to previously reported 77 %
accuracy rates in the literature. Our code is publicly available at
https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc
RECENT CNN-BASED TECHNIQUES FOR BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION
Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
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