Image decomposition with Fluorescence Microscopy Data by Ashesh

Venue: Bharti501

Abstract: Fluorescence microscopy is limited by optics, fuorophore chemistry, and photon exposure, forcing trade-ofs in speed, resolution, and depth. In this talk, I will discuss my PhD research that addresses these challenges. Specifcally, my PhD research enables imaging of multiple cellular structures within a single fuorescent channel, allowing faster imaging with less photon exposure. Technically speaking, given a superimposed image (e.g., containing nucleus and tubulin), the objective is to predict the constituent images separately.

This talk focuses on my frst work, µSplit. Early in my PhD, we found that regular deep architectures performed best with large image patches, but GPU memory limits hindered scalability. We thus developed µSplit, a novel meta-architecture enabling memory-efcient use of large image context. Built on Hierarchical-VAE (HVAE) and U-Net variants, it modifes HVAE’s ELBO loss for non-autoencoding tasks, modifes KL loss for high-frequency details extraction, and reformulates the encoder output for stable training. We also created a synthetic dataset to evaluate our network’s capability to extract large image context. Lastly, we explored tiling artifacts, analyzed two mitigation strategies, and demonstrated the superiority of one, both empirically and via out-of-distribution arguments.

Bio: Ashesh is a postdoctoral fellow at Human Technopole, Milan, Italy. He recently completed his PhD in Computer Science at TU Dresden, Germany, conducted in Florian Jug’s lab at Human Technopole’s Computational Biology Center. His doctoral research focused on image decomposition, specifcally unmixing superimposed fluorescence microscopy images into constituent channels. With frst-author publications in top CV/ML venues such as ECCV, ICCV, and NeurIPS, and a recent one accepted to Nature Methods, his work ofers a robust solution to this challenge. His thesis earned a nomination for TU Dresden’s PhD prize nominations (pending decision), an €8,700 EMBO grant for a research visit to ENS de Lyon on self-supervised fnetuning and uncertainty quantifcation, and the Best Oral Presentation Award at the 2024 HT PhD & Postdoc Symposium. Previously, Ashesh earned a dual B.Tech+M.Tech in Computer Science (2015) from IIT Delhi, India. He brings over three years of industry experience as a Data Scientist and served as Research Assistant at National Taiwan University under Prof. Hsuan-Tien Lin, initiating multiple computer vision projects, culminating in publications.