HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2210.06909v2
• Date: Mon, 17 Oct 2022 12:21:42 GMT
• Title: HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks
• Authors: Georg W\"olflein, In Hwa Um, David J Harrison, Ognjen Arandjelovi\'c
• Abstract summary: We present a framework to virtually stain Hoechst images with CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma. Our method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.
• Score: 10.043946236248392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method.

Related papers

• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images [40.347953893940044]
  We introduce a novel approach for white blood cell classification based on neural cellular automata (NCA) Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Our results demonstrate that NCA can be used for image classification, and they address key challenges of conventional methods.
  arXiv  Detail & Related papers  (2024-04-08T14:59:53Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment [7.595983383242494]
  We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. The same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplexchemistry (mIHC) This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases.
  arXiv  Detail & Related papers  (2023-05-25T20:42:23Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Machine learning based lens-free imaging technique for field-portable cytometry [0.0]
  The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types. The model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample.
  arXiv  Detail & Related papers  (2022-03-02T07:09:29Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare [77.99636165307996]
  We classify biomedical images using ensembles of neural networks. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish, Mish, Mexican Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign.
  arXiv  Detail & Related papers  (2020-11-24T01:53:39Z)
• Attention based Multiple Instance Learning for Classification of Blood Cell Disorders [38.086308180994976]
  We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. With the features extracted for each cell, a multiple instance learning method classifies patient samples into one out of four blood cell disorders. The attention mechanism provides a measure of the contribution of each cell to the overall classification and significantly improves the network's classification accuracy as well as its interpretability for the medical expert.
  arXiv  Detail & Related papers  (2020-07-22T19:29:40Z)
• Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform [0.5599792629509229]
  We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images. The method utilizes multiple markers stained in situ on a single tissue section on a robust hyperplex immunofluorescence platform.
  arXiv  Detail & Related papers  (2020-07-18T16:45:32Z)

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.