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"content": "<p><a href=\"https://mathstodon.xyz/tags/PhysicsJournalClub\" class=\"mention hashtag\" rel=\"tag\">#<span>PhysicsJournalClub</span></a> <br />"Model-free estimation of the Cramér–Rao bound for deep learning microscopy in complex media"<br />by I. Starshynov et al.</p><p>Nat. Photon. (2025)<br /><a href=\"https://doi.org/10.1038/s41566-025-01657-6\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.1038/s41566-025-016</span><span class=\"invisible\">57-6</span></a></p><p>As everybody who ever tried to orient themselves while immersed in thick fog knows, scattering scrambles information. The question "how much information is still there?" is not particularly interesting as the answer is "essentially all of it", as elastic scattering can't destroy information. A much more interesting question is "how much information can we retrieve?" In order to even try to give an answer we need to be a bit more specific, so the authors placed a small reflective surface behind a scattering layer and asked how much information about its transverse position could be retrieved. This is a well-posed question, and the answer takes the form of a "Cramér–Rao bound" (<a href=\"https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">en.wikipedia.org/wiki/Cram%C3%</span><span class=\"invisible\">A9r%E2%80%93Rao_bound</span></a>).<br />After estimating this upper bound, the authors investigate how well a trained neural network can do at this task, and show that a specifically built convolutional neural network can almost reach the theoretical bound.</p><p>[Conflict of interest: Ilya Starshynov (the first author) did his PhD in my group.]</p><p><a href=\"https://mathstodon.xyz/tags/Physics\" class=\"mention hashtag\" rel=\"tag\">#<span>Physics</span></a> <a href=\"https://mathstodon.xyz/tags/InformationTheory\" class=\"mention hashtag\" rel=\"tag\">#<span>InformationTheory</span></a> <a href=\"https://mathstodon.xyz/tags/Optics\" class=\"mention hashtag\" rel=\"tag\">#<span>Optics</span></a> <a href=\"https://mathstodon.xyz/tags/MachineLearning\" class=\"mention hashtag\" rel=\"tag\">#<span>MachineLearning</span></a></p>",
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"en": "<p><a href=\"https://mathstodon.xyz/tags/PhysicsJournalClub\" class=\"mention hashtag\" rel=\"tag\">#<span>PhysicsJournalClub</span></a> <br />"Model-free estimation of the Cramér–Rao bound for deep learning microscopy in complex media"<br />by I. Starshynov et al.</p><p>Nat. Photon. (2025)<br /><a href=\"https://doi.org/10.1038/s41566-025-01657-6\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.1038/s41566-025-016</span><span class=\"invisible\">57-6</span></a></p><p>As everybody who ever tried to orient themselves while immersed in thick fog knows, scattering scrambles information. The question "how much information is still there?" is not particularly interesting as the answer is "essentially all of it", as elastic scattering can't destroy information. A much more interesting question is "how much information can we retrieve?" In order to even try to give an answer we need to be a bit more specific, so the authors placed a small reflective surface behind a scattering layer and asked how much information about its transverse position could be retrieved. This is a well-posed question, and the answer takes the form of a "Cramér–Rao bound" (<a href=\"https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">en.wikipedia.org/wiki/Cram%C3%</span><span class=\"invisible\">A9r%E2%80%93Rao_bound</span></a>).<br />After estimating this upper bound, the authors investigate how well a trained neural network can do at this task, and show that a specifically built convolutional neural network can almost reach the theoretical bound.</p><p>[Conflict of interest: Ilya Starshynov (the first author) did his PhD in my group.]</p><p><a href=\"https://mathstodon.xyz/tags/Physics\" class=\"mention hashtag\" rel=\"tag\">#<span>Physics</span></a> <a href=\"https://mathstodon.xyz/tags/InformationTheory\" class=\"mention hashtag\" rel=\"tag\">#<span>InformationTheory</span></a> <a href=\"https://mathstodon.xyz/tags/Optics\" class=\"mention hashtag\" rel=\"tag\">#<span>Optics</span></a> <a href=\"https://mathstodon.xyz/tags/MachineLearning\" class=\"mention hashtag\" rel=\"tag\">#<span>MachineLearning</span></a></p>"
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