Tag: detection

Why cancer screening is the last thing you should pick first to work on with AI

Why cancer screening is the last thing you should pick first to work on with AI

I came across this opinionated though informed commentary by Vinay Prasad on the recent Nature article where Google’s machine learning experts trained models to predict whether scans of patients’ breasts (mammogram’s) show cancerous cells or not.

Vinay Prasad [official bio] is a practicing hematologist-oncologist and Associate Professor of Medicine at Oregon Health and Science University. So he knows what he’s talking about.

He argues that “cancer screening is the LAST thing you should pick FIRST to work on with AI”. Which is an interesting statement in and of itself.

Regardless of my personal opinion on the topic, I found the paper, Vinay’s commentary, and the broader discussion on twitter very interesting and educational to read. I feel it shows how important it is to know the context in which you are applying machine learning. What tremendous value it provides to have domain experts in the same team as the data and machine learning experts.

I cannot explain this better than Vinay himself, so please have a read of the original twitter thread here:

If you’re interested in this kind of topics, I wrote about IBM’s Watson adventures in health analytics a few years back: https://paulvanderlaken.com/2017/09/12/ibms-watson-for-oncology-a-biased-and-unproven-recommendation-system-in-cancer-treatment/

An excerpt from the twitter thread
Anomaly Detection Resources

Anomaly Detection Resources

Carnegie Mellon PhD student Yue Zhao collects this great Github repository of anomaly detection resources: https://github.com/yzhao062/anomaly-detection-resources

The repository consists of tools for multiple languages (R, Python, Matlab, Java) and resources in the form of:

  1. Books & Academic Papers
  2. Online Courses and Videos
  3. Outlier Datasets
  4. Algorithms and Applications
  5. Open-source and Commercial Libraries/Toolkits
  6. Key Conferences & Journals

Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.


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