Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge

• URL: http://arxiv.org/abs/2406.15716v1
• Date: Sat, 22 Jun 2024 03:10:23 GMT
• Title: Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
• Authors: Han Liu, Hao Li, Jiacheng Wang, Yubo Fan, Zhoubing Xu, Ipek Oguz,
• Abstract summary: We propose a deep learning-based in silico labeling method for the Light My Cells challenge. Our method achieves promising performance for in silico labeling.
• Score: 12.373115873950296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Patch-Based Encoder-Decoder Architecture for Automatic Transmitted Light to Fluorescence Imaging Transition: Contribution to the LightMyCells Challenge [2.1640167354628046]
  Automatic prediction of fluorescently labeled organelles from label-free transmitted light input images is an important, yet difficult task. We propose an algorithm that automatically predicts the fluorescently labeled nucleus, mitochondria, tubulin, and actin, based on the input consisting of bright field, phase contrast, or differential interference contrast microscopic images.
  arXiv  Detail & Related papers  (2024-06-03T10:49:34Z)
• Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation [58.03255076119459]
  We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions. Experiments on Pascal-5i and COCO-20i demonstrate significant performance gains in a variety of supervision settings.
  arXiv  Detail & Related papers  (2023-07-07T06:16:43Z)
• Self-supervised pseudo-colorizing of masked cells [18.843372840624077]
  We introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection.
  arXiv  Detail & Related papers  (2023-02-12T18:16:51Z)
• Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation [0.6465251961564605]
  We propose to use In Silico Labeling (ISL) as a pretraining scheme for segmentation tasks. By comparing segmentation performance across several training set sizes, we show that such a scheme can dramatically reduce the number of required annotations.
  arXiv  Detail & Related papers  (2023-01-10T11:35:14Z)
• Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images [57.42492501915773]
  In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
  arXiv  Detail & Related papers  (2022-08-05T18:52:20Z)
• Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations [72.15956198507281]
  We propose PGV-CL, a novel pseudo-label guided cross-video contrast learning method to boost scene segmentation. We extensively evaluate our method on a public robotic surgery dataset EndoVis18 and a public cataract dataset CaDIS.
  arXiv  Detail & Related papers  (2022-07-20T05:42:19Z)
• Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation [11.935891325600952]
  We propose a supervised local contrastive loss that leverages limited pixel-wise annotation to force pixels with the same label to gather around in the embedding space. With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
  arXiv  Detail & Related papers  (2021-09-15T16:23:48Z)
• Mixed Supervision Learning for Whole Slide Image Classification [88.31842052998319]
  We propose a mixed supervision learning framework for super high-resolution images. During the patch training stage, this framework can make use of coarse image-level labels to refine self-supervised learning. A comprehensive strategy is proposed to suppress pixel-level false positives and false negatives.
  arXiv  Detail & Related papers  (2021-07-02T09:46:06Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes [51.17758371472664]
  Photoswitchable molecules display two or more isomeric forms that may be accessed using light. We present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules.
  arXiv  Detail & Related papers  (2020-06-28T20:59:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.