Biomedical Image Reconstruction: A Survey
It provides a foundational overview for machine learning researchers to understand the field, but it is incremental as it compiles existing knowledge without introducing new methods.
This survey paper tackles the problem of summarizing the evolution and current trends in biomedical image reconstruction, particularly the shift from traditional methods to deep learning techniques, by reviewing publications from the MLMIR workshop since 2018.
Biomedical image reconstruction research has been developed for more than five decades, giving rise to various techniques such as central and filtered back projection. With the rise of deep learning technology, biomedical image reconstruction field has undergone a massive paradigm shift from analytical and iterative methods to deep learning methods To drive scientific discussion on advanced deep learning techniques for biomedical image reconstruction, a workshop focusing on deep biomedical image reconstruction, MLMIR, is introduced and is being held yearly since 2018. This survey paper is aimed to provide basic knowledge in biomedical image reconstruction and the current research trend in biomedical image reconstruction based on the publications in MLMIR. This survey paper is intended for machine learning researchers to grasp a general understanding of the biomedical image reconstruction field and the current research trend in deep biomedical image reconstruction.