The rise in popularity of deep learning is revolutionizing the way biomedical images are acquired, processed, analyzed. Just a few years ago, extracting high-level understanding from biomedical images was a process restricted to highly trained professionals often requiring multidisciplinary collaborations. In the work presented, we showcase a study that compares the performance of a model trained end-to-end using a novel deep learning architecture, versus a model trained on its corresponding state-of-the-art mathematically engineered feature. Results show that end-to-end deep learning significantly outperforms the mathematical model, suggesting that feature engineering will play a less important role in the coming years.
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