5,061 research outputs found
Deep learning based tissue analysis predicts outcome in colorectal cancer
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low-and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.Peer reviewe
Hyperspectral colon tissue cell classification
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Efficient and precise classification of histological cell nuclei is of utmost
importance due to its potential applications in the field of medical image
analysis. It would facilitate the medical practitioners to better understand
and explore various factors for cancer treatment. The classification of
histological cell nuclei is a challenging task due to the cellular
heterogeneity. This paper proposes an efficient Convolutional Neural Network
(CNN) based architecture for classification of histological routine colon
cancer nuclei named as RCCNet. The main objective of this network is to keep
the CNN model as simple as possible. The proposed RCCNet model consists of only
1,512,868 learnable parameters which are significantly less compared to the
popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The
experiments are conducted over publicly available routine colon cancer
histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet
model are compared with five state-of-the-art CNN models in terms of the
accuracy, weighted average F1 score and training time. The proposed method has
achieved a classification accuracy of 80.61% and 0.7887 weighted average F1
score. The proposed RCCNet is more efficient and generalized terms of the
training time and data over-fitting, respectively.Comment: Published in ICARCV 201
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