scholarly journals Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

10.29007/n912 ◽  
2019 ◽  
Author(s):  
Xingzhi Yue ◽  
Neofytos Dimitriou ◽  
Peter Caie ◽  
David Harrison ◽  
Ognjen Arandjelovic
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Health Informatics ◽  
Digital Pathology ◽  
Health Impact ◽  
Research Area ◽  
Real World Data ◽  
Data Set ◽  
Research Attention ◽  
Cancer Outcome

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in digital oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, and numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole haematoxylin & eosin (H&E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals Performance of Machine Learning Algorithms and Diversity in Data

2018 ◽  
Vol 210 ◽  
pp. 04019 ◽  
Author(s):  
Hyontai SUG
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Real World Data ◽  
Random Data ◽  
Data Set ◽  
World Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.

AN ANALYSIS OF MULTIPLE SIMILARITY MEASURES FOR ONTOLOGY MAPPING PROBLEM

2010 ◽  
Vol 04 (01) ◽  
pp. 103-122 ◽  
Author(s):  
RYUTARO ICHISE
Keyword(s):  
Machine Learning ◽  
Similarity Measures ◽  
Ontology Mapping ◽  
Support Vector ◽  
Real World Data ◽  
Data Set ◽  
Mapping Problem ◽  
Knowledge Based ◽  
Analysis Of Similarity ◽  
Mapping Systems

This paper presents an analysis of similarity measures for the ontology mapping problem. To that end, 48 similarity measures such as string matching and knowledge based similarities that have been widely used in ontology mapping systems are defined. The similarity measures are investigated by discriminant analysis with a real-world data set. As a result, it was possible to identify 22 effective similarity measures for the ontology mapping problem out of 48 possible similarity measures. The identified measures have a wide variety in the type of similarity. To test whether the identified similarity measures are effective for the problem, experiments were conducted with all 48 similarity measures and the 22 identified similarity measures by using two major machine learning methods, decision tree and support vector machine. The experimental results show that the performance of the 48 cases and the 22 cases is almost the same regardless of the machine learning method. This implies that effective features for the ontology mapping problem were successfully identified.

scholarly journals A survey on feature weighting based K-Means algorithms

2020 ◽  
Author(s):  
Renato Cordeiro de Amorim
Keyword(s):  
Machine Learning ◽  
Empirical Evidence ◽  
Real World ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Feature Weighting ◽  
Future Research ◽  
Real World Data ◽  
Data Set ◽  
Partitional Clustering

In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means

scholarly journals Association of baseline absolute neutrophil counts and survival in patients with metastatic colorectal cancer treated with second-line antiangiogenic therapies: exploratory analyses of the RAISE trial and validation in an electronic medical record data set

ESMO Open ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. e000347 ◽  
Author(s):  
Axel Grothey ◽  
Takayuki Yoshino ◽  
Gyorgy Bodoky ◽  
Tudor Ciuleanu ◽  
Rocio Garcia-Carbonero ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Real World Data ◽  
Data Set ◽  
High Baseline ◽  
Registration Number ◽  
Chemotherapy Dose ◽  
Dose Modifications

BackgroundIn the RAISE trial, ramucirumab+leucovorin/fluorouracil/irinotecan (FOLFIRI) improved the median overall survival (mOS) of patients with previously treated metastatic colorectal cancer versus patients treated with placebo+FOLFIRI but had a higher incidence of neutropaenia, leading to more chemotherapy dose modifications and discontinuations. Thus, we conducted an exploratory post-hoc analysis of RAISE and a retrospective, observational analysis of electronic medical record (EMR) data to determine and verify the association of neutropaenia, baseline absolute neutrophil count (ANC) and survival.MethodsThe RAISE analysis used the study safety population (n=1057). IMS Health Oncology Database (IMS EMR) was the source for the real-world data set (n=617).ResultsRAISE patients with treatment-emergent neutropaenia had improved mOS compared with those without (ramucirumab arm: 16.1 vs 10.7 months, HR=0.57, p<0.0001; placebo arm: 12.7 vs 10.7 months, HR=0.76, p=0.0065). RAISE patients with low ANC versus high baseline ANC also had longer mOS (ramucirumab arm: 15.2 vs 8.9 months, HR=0.49, p<0.0001; placebo arm: 13.2 vs 7.3 months, HR=0.50, p<0.0001). The results were similar for IMS EMR low versus high baseline ANC (bevacizumab+FOLFIRI patients: 14.9 vs 7.7 months, HR=0.59, p<0.0001; FOLFIRI alone: 14.6 vs 5.4 months, HR=0.37, p<0.0001). Patients in the RAISE trial with low baseline ANC were more likely to develop neutropaenia (OR: ramucirumab arm=2.62, p<0.0001; placebo arm=2.16, p=0.0003).ConclusionNeutropaenia during treatment, and subsequent dose modifications or discontinuations, do not compromise treatment efficacy. Baseline ANC is a strong prognostic factor for survival and is associated with treatment-emergent neutropaenia in the analysed population.Trial registration numberNCT01183780, Results.

A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow

2021 ◽  
pp. 1-4
Author(s):  
Gusheng Tang ◽  
Xinyan Fu ◽  
Zhen Wang ◽  
Mingyi Chen
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Cell Lymphoma ◽  
Digital Pathology ◽  
B Cell Lymphoma ◽  
Lymphoid Cells ◽  
Learning System ◽  
Training Data ◽  
Digital Microscopy ◽  
Data Set

Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.

scholarly journals Prediction of Soccer Matches using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 873-877
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Error Rates ◽  
Online Game ◽  
Web Crawler ◽  
Real World Data ◽  
Data Set ◽  
The Real ◽  
Different Types ◽  
Linear Svm

The paper is concerned with predicting the result of a League and creating Strategies from gathered data using Machine Learning and Artificial Intelligence algorithms. Here we are taking data set from the real life game stats and from the massive multiplayer game series FIFA and PES. We will start by creating a web crawler to collect data-set and compare both the real world data and virtual data (Online game data) to predict the outcome of the match using supervised and unsupervised learning. Using K means clustering to segregate between different types of players such as offensive players, defensive players and goalkeepers using game data, then normalizing the virtual features to predict team strategies. We will use different models like Gaussian Naive Bayes, Hidden Markov model, Linear SVM etc. to reduce the error rates and increasing our accuracy to predict matches. This implementation can help all the teams to devise strategies for opposing team by knowing their strategies. This can also help to predict the winner of the league outcome. This prediction model can also be used in predicting Stock Market, Coaching improvements, journalism etc

Machine Learning for Clinical Data Processing

2011 ◽  
pp. 193-215
Author(s):  
Guo-Zheng Li
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Clinical Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Sets ◽  
The Novel ◽  
Real World Data ◽  
Data Set ◽  
Learning Techniques

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

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