On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data
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Abstract (arXiv)
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information, and the importance of learning local features. Based on our findings, we propose two adaptations to the current state-of-the-art (SOTA) model. Our contributions include an enhanced SOTA model, demonstrating superior performance on publicly available datasets and improved generalization across laboratories, as well as valuable insights for the FCM community, guiding future DL architecture designs for FCM data analysis. The code is available at urlhttps://github.com/lisaweijler/flowNetworks.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
- 2022cosFormer: Rethinking Softmax in Attentioncited by 213
- 2022Representing Long-Range Context for Graph Neural Networks with Global Attentioncited by 195
- 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
- 2023FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry datacited by 2
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