3 ms·
> In my "physics of biomedical imaging" bubble, people are hardly doing state-of-the-art ML, but rather expensive forward models for which computing a gradient
by ssivark 3y ago
> In my "physics of biomedical imaging" bubble, people are hardly doing state-of-the-art ML, but rather expensive forward models for which computing a gradient is cumbersome.
I’d appreciate any pointers to the literature; curious to see the kinds of models people work with. Thanks!
- cl3misch 3y agoI don't have didactic examples at hand, but e.g. [1] or [2]. IIRC [1] uses the Laplace operator (second-order spatial derivate) and [2] uses a linear solve inside the forward model through which differentiation is certainly possible but pretty cumbersome in practice. [1] https://www.nature.com/articles/s41598-019-52283-6 https://www.nature.com/articles/s41598-019-52283-6 [2] https://doi.org/10.1117/1.JMI.4.3.034005 https://doi.org/10.1117/1.JMI.4.3.034005