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The field of pathology has been revolutionized by the introduction of machine learning techniques, which enable more efficient and accurate diagnoses and have the potential to some day even eliminate or reduce the number of expensive molecular tests. However, the model development is a complex process and  there are certain mistakes that must be avoided when using machine learning for pathology. 

In this informative discussion with Heather Couture, an expert in machine learning for pathology, she highlights the top 5 MISTAKES THAT YOU MUSTAVOID to ensure the best possible machine learning and deep learning project outcomes.

Through her insights, you will learn about the 5 most common ML mistakes and how to avoid them:

1.  Not understanding your data and its challenges.
2. Diving in without researching prior work (academic research and open source code) that is similar to what you're trying to model.
3. Starting with too complex a model.
4. Not thinking ahead towards validation.
5. Not fully understanding how the technology will ultimately be used.

By avoiding these common mistakes, you can maximize the benefits of machine learning for pathology and ensure accurate and timely project results and product launches. Whether you are new to machine learning or an experienced practitioner, this discussion is a valuable resource for anyone interested in using machine learning (including deep learning) for pathology.

THIS EPISODE'S RESOURCES:

📰 Heather's amazing newsletter (Computer Vision Insights)
🎧 Heather's fantastic podcast "Impact AI"
🎙️ Aleks' previous podcast with Heather (1) - Why machine learning expertise is needed for digital pathology projects
🎙️ Aleks' previous podcast with Heather - How to make machine learning models more robust

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