Image decomposition in Fluorescence Microscopy: A posterior sampling based approach
Ashesh Ashesh (PHD Student, Human Technopole, Milano, Italy)
| When |
Mar 18, 2025
from 01:00 to 02:00 |
|---|---|
| Where | M7 101 |
| Attendees |
Ashesh Ashesh |
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Ashesh Ashesh
Title: Image decomposition in Fluorescence Microscopy: A posterior sampling based approach
Abstract: Fluorescence microscopy faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits. This necessitates trade-offs in imaging speed, resolution, and depth. In my talk, I will discuss the two deep-learning-based computational multiplexing techniques [AKDS+23, AJ24], and their application [ACZ+25] which we developed during my PhD that enhanced the imaging of multiple cellular structures within a single fluorescent channel, allowing faster imaging and reduced photon exposure. Given a superimposed image (say containing Nucleus and Tubulin), my PhD research is to predict its constituent images separately. Our approach can sample diverse predictions from a trained posterior and is GPU-efficient. At last, I will end my talk with our ongoing work on creating an approach which can handle different levels of superposition
Refs[ACZ+25] Ashesh, Florian Jug et al. Microsplit: Semantic unmixing of fluorescent microscopy data. bioRxiv, 2025.[AJ24] Ashesh and Florian Jug. denoisplit: a method for joint microscopy image splitting and unsupervised denoising. ECCV 2024, 2024.[AKDS+23] Ashesh, Alexander Krull, Moises Di Sante, Francesco Pasqualini, and Florian Jug. usplit: Image decomposition for fluorescence microscopy. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 21219–21229, October 2023.
Website: https://ashesh-0.github.io/
In Room M7 101, 1st floor, Monod campus, ENSL.