Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder

no code implementations • 20 Jul 2022 • Zheng Chen, Ziwei Yang, Lingwei Zhu, Guang Shi, Kun Yue, Takashi Matsubara, Shigehiko Kanaya, MD Altaf-Ul-Amin

As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

Clustering

Automated Sleep Staging via Parallel Frequency-Cut Attention

no code implementations • 7 Apr 2022 • Zheng Chen, Ziwei Yang, Lingwei Zhu, Wei Chen, Toshiyo Tamura, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.

Decision Making EEG +2

Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data

no code implementations • 2 Apr 2022 • Ziwei Yang, Lingwei Zhu, Zheng Chen, Ming Huang, Naoaki Ono, MD Altaf-Ul-Amin, Shigehiko Kanaya

In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.

Quantization