Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
This addresses the problem of invasive and time-consuming fluorescence labeling for microscopy, offering a non-invasive alternative, though it appears incremental as it builds on existing methods.
The paper tackled predicting fluorescent labels from label-free microscopy images using a pix2pix-based method with adaptive loss, achieving promising performance in the Light My Cells challenge.
Fluorescence labeling is the standard approach to reveal cellular structures and other subcellular constituents for microscopy images. However, this invasive procedure may perturb or even kill the cells and the procedure itself is highly time-consuming and complex. Recently, in silico labeling has emerged as a promising alternative, aiming to use machine learning models to directly predict the fluorescently labeled images from label-free microscopy. In this paper, we propose a deep learning-based in silico labeling method for the Light My Cells challenge. Built upon pix2pix, our proposed method can be trained using the partially labeled datasets with an adaptive loss. Moreover, we explore the effectiveness of several training strategies to handle different input modalities, such as training them together or separately. The results show that our method achieves promising performance for in silico labeling. Our code is available at https://github.com/MedICL-VU/LightMyCells.