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A machine learning model (simple or neural net) is agnostic of the image type or the object depicted. That is because in both cases images consist of pixels (i.e a matrix of numbers with a certain range) and a CNN model, for instance, will identify a dog or a drawing based on the image's pixels pattern (in a very high level). Therefore, both tasks can be ...


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Check this paper. Its introduction gives a very good definition of both: The classic approach towards the assessment of any machine learning model revolves around the evaluation of its generalizability i.e. its performance on unseen test scenarios. Evaluating such models on an available non-overlapping test set is popular, yet significantly limited in its ...


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You will get the best results if the new images are preprocessed in the exact same way the training images were preprocessed. The biggest issue is resizing the images to be the same size as the input layer expects.


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I would recommend the brand new "AI for Medical Diagnosis" course on Coursera. In this course, you will learn how to apply Machine Learning (ML) techniques to concrete problems in modern medicine. Moreover, it focuses on several medical imaging problems. You can also look at some of our works [1, 2, 3], where we introduce these topics across the ...


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Several studies demonstrated [1, 2] that radiologist fatigue levels and performance are related to environmental factors such as number of False-Positives and False-Negatives. However, the number of False-Positives is usually higher than the number of False-Negatives if we consider solely the diagnosis (e.g., breast, lung cancers, etc.) over medical imaging ...


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