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This paper presents an original framework based on deep learning and preference learning to retrieve and characterize biomedical images for assisting physicians in diagnosing complex diseases with potentially only small differences between them. In particular, we use deep learning to extract the high-level and compact features for biomedical images. In contrast to the traditional biomedical algorithms...
The problem of bridging the gap between efficient but narrow methods of machine learning, and universal but inefficient methods was considered. Our main claim, which is methodologically important to the field of Artificial General Intelligence (AGI), is that neither narrow nor basic universal methods are sufficient for AGI. This claim was illustrated on example of pattern recognition task using stacked...
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