Machine Learning Based Classification of Colorectal Cancer Tumour Tissue in Whole-Slide Images

Informatica ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Mindaugas Morkūnas ◽  
Povilas Treigys ◽  
Jolita Bernatavičienė ◽  
Arvydas Laurinavičius ◽  
Gražina Korvel
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Tumour Tissue ◽  
Whole Slide Images

scholarly journals Different Machine Learning and Deep Learning Methods for the Classification of Colorectal Cancer Lymph Node Metastasis Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Jin Li ◽  
Peng Wang ◽  
Yang Zhou ◽  
Hong Liang ◽  
Kuan Luan
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Transfer Learning ◽  
Raw Data ◽  
Node Metastasis ◽  
Treatment Plans

The classification of colorectal cancer (CRC) lymph node metastasis (LNM) is a vital clinical issue related to recurrence and design of treatment plans. However, it remains unclear which method is effective in automatically classifying CRC LNM. Hence, this study compared the performance of existing classification methods, i.e., machine learning, deep learning, and deep transfer learning, to identify the most effective method. A total of 3,364 samples (1,646 positive and 1,718 negative) from Harbin Medical University Cancer Hospital were collected. All patches were manually segmented by experienced radiologists, and the image size was based on the lesion to be intercepted. Two classes of global features and one class of local features were extracted from the patches. These features were used in eight machine learning algorithms, while the other models used raw data. Experiment results showed that deep transfer learning was the most effective method with an accuracy of 0.7583 and an area under the curve of 0.7941. Furthermore, to improve the interpretability of the results from the deep learning and deep transfer learning models, the classification heat-map features were used, which displayed the region of feature extraction by superposing with raw data. The research findings are expected to promote the use of effective methods in CRC LNM detection and hence facilitate the design of proper treatment plans.

TISSUE CLASSIFICATION FOR COLORECTAL CANCER UTILIZING TECHNIQUES OF DEEP LEARNING AND MACHINE LEARNING

2021 ◽  
pp. 2150022
Author(s):  
Kasikrit Damkliang ◽  
Thakerng Wongsirichot ◽  
Paramee Thongsuksai
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Texture Classification ◽  
False Positive Rate ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Cancer Tissues

Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.

scholarly journals Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia

2020 ◽  
pp. 1-9
Author(s):  
Pablo Bascuñana ◽  
Mirjam Brackhan ◽  
Jens Pahnke
Keyword(s):  
Machine Learning ◽  
Traditional Approach ◽  
Amyloid Β ◽  
Cell Types ◽  
Current Standard ◽  
Activated Microglia ◽  
Total Analysis ◽  
Quantitative Results ◽  
Whole Slide Images

Background: Detailed pathology analysis and morphological quantification is tedious, prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIOTM for automatic analysis of histological whole-slide images using machine learning/artificial intelligence. Objective: To evaluate and validate the use of DeePathology STUDIO for the analysis of histological slides at high resolution. Methods: We compared the DeePathology STUDIO and our current standard method using macros in AxioVision for the analysis of amyloid-β (Aβ) plaques and microglia in APP-transgenic mice at different ages. We analyzed density variables and total time invested with each approach. In addition, we correlated Aβ concentration in brain tissue measured by ELISA with the results of Aβ staining analysis. Results: DeePathology STUDIO showed a significant decrease of the time for establishing new analyses and the total analysis time by up to 90% . On the other hand, both approaches showed similar quantitative results in plaque and activated microglia density in the different experimental groups. DeePathology STUDIO showed higher sensitivity and accuracy for small-sized plaques. In addition, DeePathology STUDIO allowed the classification of plaques in diffuse- and dense-packed, which was not possible with our traditional analysis. Conclusion: DeePathology STUDIO reduced substantially the effort needed for a new analysis showing comparable quantitative results to the traditional approach. In addition, it allowed including different objects (categories) or cell types in a single analysis, which is not possible with conventional methods.

Tissue microarray based classification of early and late stage colorectal cancer in optical and virtual microscopy

2007 ◽  
Vol 45 (05) ◽  
Author(s):  
F Sipos ◽  
S Spisák ◽  
T Krenács ◽  
O Galamb ◽  
B Galamb ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tissue Microarray ◽  
Late Stage ◽  
Virtual Microscopy

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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