Machine Learning Assessment & Roadmap

Machine learning can add power to your analysis of pathology or remote sensing images:

  • Assist expert by improving efficiency, precision, and repeatability
  • Learn concepts beyond human capabilities like molecular biomarkers, patient outcomes, or treatment response directly from images

Using my proprietary process, I'll author a detailed analysis of your ML algorithms for quantifying images. This report will outline how to advance your project using the latest state-of-the-art techniques.


The report will detail each of the components below and make recommendations such as the following:

1. Images and annotations

  • Anticipated challenges due to dataset size, image size and appearance, availability and accuracy of annotations, additional modalities of data, or other unique properties
  • Requirements for preprocessing and cleaning data
  • Additional data that could be used to improve model (e.g., public data sets, additional modalities of data)
  • Model generalizability considerations

2. Related work

  • References to existing literature that can guide expectations
  • Links to relevant toolkits

3. Metrics

  • Possible metrics and considerations in selection
  • Training, validation, and test set creation

4. Modeling

  • Suggested first modeling efforts and directions for further improvement
  • Recommendations for better modeling of unique aspects of the problem or incorporating additional data
  • Model modifications to improve generalizability
  • Transfer learning strategies to improve model initialization
  • Image augmentation requirements
  • Optimization strategies to improve model training

5. Analysis

  • Procedure for validating model on held out test set
  • Steps for performing an error analysis to guide directions for data cleaning or model improvement

The Process:

1. Discovery call

Let's hop on a call to learn more about each other. We'll discuss where you are now and where you need to go, what's working well and what you're struggling with.

2. Data and info gathering

I'll talk with your technical team about your current data pipeline and algorithms. I'll likely ask for any existing documentation, data samples, and possibly some code.

3. Report generation

Guided by the 5 components above, I'll write a report outlining the strengths of your current data and modeling approach and recommendations for further advancements. I'll meet weekly with you or your technical team via Zoom during this phase to review progress on the roadmap and answer any questions.

4. Review

Upon completion of the report, we'll have a Zoom call for a final review and to ensure that you are confident in the next steps for your project.


Results:

  • An understanding of the key components for success with machine learning
  • A set of actions that can be implemented and tested by in-house data and ML engineers
  • Confidence in the success of the project