John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 , 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 repo using the same design principles. These include - a flexible data processing pipeline, - reusable Deep Learning (DL) model architecture components and loss functions, and - a library for evaluating ML models.
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bemali Wickramanayake, Zhipeng He, et al.
Knowledge-Based Systems
Michael Hersche, Mustafa Zeqiri, et al.
NeSy 2023
Joseph Y. Halpern
aaai 1996