Predicting survival from colorectal cancer histology slides using #deeplearning
1. They conducted the study because:
• Colorectal cancer (CRC) is a common disease with a variable clinical course, and there is a high clinical need to more accurately predict the outcome of individual patients.
• For almost every CRC patient, histological slides of tumor tissue are routinely available.
• Deep learning can be used to extract information from very complex images, and we hypothesized that deep learning can predict clinical outcome directly from histological images of CRC.
2. What did the researchers do and find?
• We trained a deep neural network to identify different tissue types & demonstrated that it can decompose complex tissue into its constituent parts and thereby showed that this score improves survival prediction compared to the SOTA avaiable.
3. Conclusion
• Deep learning is an inexpensive tool to predict the clinical course of CRC patients based on ubiquitously available histological images.
• Prospective validation studies are needed to firmly establish this biomarker for routine clinical use.
☞ Link to research
#healthcare #AI #machinelearning
❇️ @AI_Python_EN
1. They conducted the study because:
• Colorectal cancer (CRC) is a common disease with a variable clinical course, and there is a high clinical need to more accurately predict the outcome of individual patients.
• For almost every CRC patient, histological slides of tumor tissue are routinely available.
• Deep learning can be used to extract information from very complex images, and we hypothesized that deep learning can predict clinical outcome directly from histological images of CRC.
2. What did the researchers do and find?
• We trained a deep neural network to identify different tissue types & demonstrated that it can decompose complex tissue into its constituent parts and thereby showed that this score improves survival prediction compared to the SOTA avaiable.
3. Conclusion
• Deep learning is an inexpensive tool to predict the clinical course of CRC patients based on ubiquitously available histological images.
• Prospective validation studies are needed to firmly establish this biomarker for routine clinical use.
☞ Link to research
#healthcare #AI #machinelearning
❇️ @AI_Python_EN
Deep Learning:
http://course.fast.ai
NLP:
http://bit.ly/fastai-nlp
Comp Linear Algebra:
http://github.com/fastai/numerical-linear-algebra
Bias, Ethics, & AI:
http://fast.ai/topics/#ai-in-society
Debunk Pipeline Myth:
http://bit.ly/not-pipeline
AI Needs You:
http://bit.ly/rachel-TEDx
Ethics Center:
http://bit.ly/USF-CADE
❇️ @AI_Python_EN
http://course.fast.ai
NLP:
http://bit.ly/fastai-nlp
Comp Linear Algebra:
http://github.com/fastai/numerical-linear-algebra
Bias, Ethics, & AI:
http://fast.ai/topics/#ai-in-society
Debunk Pipeline Myth:
http://bit.ly/not-pipeline
AI Needs You:
http://bit.ly/rachel-TEDx
Ethics Center:
http://bit.ly/USF-CADE
❇️ @AI_Python_EN
www.fast.ai
new fast.ai course: A Code-First Introduction to Natural Language Processing
fast.ai's newest course is Code-First Intro to NLP. It covers a blend of traditional NLP techniques, recent deep learning approaches, and urgent ethical issues.
PaperRobot: Incremental Draft Generation of Scientific Ideas
https://lnkd.in/exHGHjW
#ArtificialIntelligence #AI #MachineLearning #DeepLearning
❇ @AI_Python_EN
https://lnkd.in/exHGHjW
#ArtificialIntelligence #AI #MachineLearning #DeepLearning
❇ @AI_Python_EN
How 20th Century Fox uses ML to predict a movie audience
Google Cloud Blog
http://bit.ly/2N3I7SC
#AI #DeepLearning #MachineLearning #DataScience
✴️ @AI_Python_EN
Google Cloud Blog
http://bit.ly/2N3I7SC
#AI #DeepLearning #MachineLearning #DataScience
✴️ @AI_Python_EN
Google Cloud Blog
How 20th Century Fox uses ML to predict a movie audience | Google Cloud Blog
Success in the movie industry relies on a studio’s ability to attract moviegoers—but that’s sometimes easier said than done. Moviegoers are a diverse group
What's the purpose of statistics?
"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek
The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.
These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.
How does it do this?
One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.
Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).
Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.
These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.
Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.
Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.
Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.
The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.
"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey
#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience
❇️ @AI_Python_EN
"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek
The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.
These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.
How does it do this?
One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.
Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).
Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.
These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.
Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.
Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.
Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.
The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.
"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey
#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience
❇️ @AI_Python_EN
New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0
- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code
http://pyimg.co/5wzc5
#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision
❇️ @AI_Python_EN
- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code
http://pyimg.co/5wzc5
#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision
❇️ @AI_Python_EN
Data science is not #MachineLearning .
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.
#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data
Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.
If you love data, you will like this role....
solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.
➡️ Get free resources at his site:
www.claoudml.com
❇️ @AI_Python_EN
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.
#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data
Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.
If you love data, you will like this role....
solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.
➡️ Get free resources at his site:
www.claoudml.com
❇️ @AI_Python_EN
François Chollet (Google, Creator of Keras) just released a paper on defining and measuring intelligence and a GitHub repo that includes a new #AI evaluation dataset, ARC – "Abstraction and Reasoning Corpus".
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
#AI #machinelearning #deeplearning
❇️ @AI_Python_EN
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
#AI #machinelearning #deeplearning
❇️ @AI_Python_EN
Optimizing Millions of Hyperparameters by Implicit Differentiation Lorraine et al.:
https://arxiv.org/abs/1911.02590
#ArtificialIntelligence #MachineLearning
❇️ #AI_Python_EN
https://arxiv.org/abs/1911.02590
#ArtificialIntelligence #MachineLearning
❇️ #AI_Python_EN