Machine Learning is at the forefront of scientific progress in Healthcare and Medicine. To accelerate scientific discovery, it is important to have tools that allow progress iterations to be collaborative, reproducible, reusable and easily built upon without "reinventing the wheel" for each task. FuseMedML, or fuse, is a Python framework designed for accelerated Machine Learning (ML) based discovery in the medical domain. It is highly flexible and designed for easy collaboration, encouraging code reuse. Flexibility is enabled by a generic data object design where data is kept in a nested (hierarchical) Python dictionary (NDict), allowing to efficiently process and fuse information from multiple modalities. Functional components allow to specify input and output keys, to be read from and written to the nested dictionary. Easy code reuse is enabled through key components implemented as standalone packages under the main fuse repo using the same design principles. These include fuse.data -a flexible data processing pipeline, fuse.dl -reusable Deep Learning (DL) model architecture components and loss functions, and fuse.eval -a library for evaluating ML models.
Many studies analyze resolution limits in single-channel, pan-chromatic systems. However, color imaging is popular. Thus, there is a need for its modeling in terms of resolving capacity under noise. This work analyzes the probability of resolving details as a function of spatial frequency in color imaging. The analysis introduces theoretical bounds for performance, using optimal linear filtering and fusion operations. The work focuses on resolution loss caused strictly by noise, without the presence of imaging blur. It applies to full-field color systems, which do not compromise resolution by spatial multiplexing. The framework allows us to assess and optimize the ability of an imaging system to distinguish an object of given size and color under image noise.
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