FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data-Reference-Cited by-同舟云学术

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FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data

Published:2024-01-03 Issue: Volume: Page:
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Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Weijler Lisa¹,Kowarsch Florian¹,Reiter Michael¹,Hermosilla Pedro¹,Maurer-Granofszky Margarita²,Dworzak Michael²

Affiliation:

1. TU Wien

2. St. Anna CCRI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10483279/10483282/10483581.pdf?arnumber=10483581

Reference38 articles.

1. CyTOFmerge: integrating mass cytometry data across multiple panels

2. Predicting Cell Populations in Single Cell Mass Cytometry Data

3. Sensitive detection of rare disease-associated cell subsets via representation learning

4. MAE-AST: Masked Autoencoding Audio Spectrogram Transformer

5. Data2vec: A general framework for self-supervised learning in speech, vision and language;Baevski

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GateNet: A novel neural network architecture for automated flow cytometry gating;Computers in Biology and Medicine;2024-09

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