13 research outputs found
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
Convolutional Neural Networks (CNN) are state-of-the-art models for many
image classification tasks. However, to recognize cancer subtypes
automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images
(WSI) is currently computationally impossible. The differentiation of cancer
subtypes is based on cellular-level visual features observed on image patch
scale. Therefore, we argue that in this situation, training a patch-level
classifier on image patches will perform better than or similar to an
image-level classifier. The challenge becomes how to intelligently combine
patch-level classification results and model the fact that not all patches will
be discriminative. We propose to train a decision fusion model to aggregate
patch-level predictions given by patch-level CNNs, which to the best of our
knowledge has not been shown before. Furthermore, we formulate a novel
Expectation-Maximization (EM) based method that automatically locates
discriminative patches robustly by utilizing the spatial relationships of
patches. We apply our method to the classification of glioma and non-small-cell
lung carcinoma cases into subtypes. The classification accuracy of our method
is similar to the inter-observer agreement between pathologists. Although it is
impossible to train CNNs on WSIs, we experimentally demonstrate using a
comparable non-cancer dataset of smaller images that a patch-based CNN can
outperform an image-based CNN
TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading
While microscopic analysis of histopathological slides is generally
considered as the gold standard method for performing cancer diagnosis and
grading, the current method for analysis is extremely time consuming and labour
intensive as it requires pathologists to visually inspect tissue samples in a
detailed fashion for the presence of cancer. As such, there has been
significant recent interest in computer aided diagnosis systems for analysing
histopathological slides for cancer grading to aid pathologists to perform
cancer diagnosis and grading in a more efficient, accurate, and consistent
manner. In this work, we investigate and explore a deep triple-stream residual
network (TriResNet) architecture for the purpose of tile-level histopathology
grading, which is the critical first step to computer-aided whole-slide
histopathology grading. In particular, the design mentality behind the proposed
TriResNet network architecture is to facilitate for the learning of a more
diverse set of quantitative features to better characterize the complex tissue
characteristics found in histopathology samples. Experimental results on two
widely-used computer-aided histopathology benchmark datasets (CAMELYON16
dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the
proposed TriResNet network architecture was able to achieve noticeably improved
accuracies when compared with two other state-of-the-art deep convolutional
neural network architectures. Based on these promising results, the hope is
that the proposed TriResNet network architecture could become a useful tool to
aiding pathologists increase the consistency, speed, and accuracy of the
histopathology grading process.Comment: 9 page
Tissue object patterns for segmentation in histopathological images
In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is subject to a considerable amount of observer variability. In order to decrease the variability, it has been proposed to develop systems that mathematically model the histopathological tissue images and automate the analysis. Segmentation constitutes the first step for most of these automated systems. Nevertheless, the segmentation in histopathological images remains a challenging task since these images typically show variances due to their complex nature and may include a large amount of noise and artifacts due to the tissue preparation procedures. In our research group, we recently developed different segmentation algorithms that rely on representing a tissue image with a set of tissue objects and using the structural pattern of these objects in segmentation. In this paper, we review these segmentation algorithms, discussing their clinical demonstrations on colon tissues. © 2011 ACM