Advances in artificial intelligence (AI) show great potential in revealing
underlying information from phonon microscopy (high-frequency ultrasound) data
to identify cancerous cells. However, this technology suffers from the 'batch
effect' that comes from unavoidable technical variations between each
experiment, creating confounding variables that the AI model may inadvertently
learn. We therefore present a multi-task conditional neural network framework
to simultaneously achieve inter-batch calibration, by removing confounding
variables, and accurate cell classification of time-resolved phonon-derived
signals. We validate our approach by training and validating on different
experimental batches, achieving a balanced precision of 89.22% and an average
cross-validated precision of 89.07% for classifying background, healthy and
cancerous regions. Classification can be performed in 0.5 seconds with only
simple prior batch information required for multiple batch corrections.
Further, we extend our model to reconstruct denoised signals, enabling physical
interpretation of salient features indicating disease state including sound
velocity, sound attenuation and cell-adhesion to substrate.

通过使用条件神经网络的多任务模型，结合人工智能和声子显微镜技术，来研究和识别高频超声数据中的癌细胞；该方法能够校正批次效应、进行准确的细胞分类，并重构降噪信号，以实现疾病状态的物理解释。

利用多任务条件神经网络进行扩展批次校准的声子显微镜可解释癌细胞检测

Interpretable cancer cell detection with phonon microscopy using  multi-task conditional neural networks for inter-batch calibration

Our work introduces an innovative approach to graph learning by leveraging
Hyperdimensional Computing. Graphs serve as a widely embraced method for
conveying information, and their utilization in learning has gained significant
attention. This is notable in the field of chemoinformatics, where learning
from graph representations plays a pivotal role. An important application
within this domain involves the identification of cancerous cells across
diverse molecular structures.
We propose an HDC-based model that demonstrates comparable Area Under the
Curve results when compared to state-of-the-art models like Graph Neural
Networks (GNNs) or the Weisfieler-Lehman graph kernel (WL). Moreover, it
outperforms previously proposed hyperdimensional computing graph learning
methods. Furthermore, it achieves noteworthy speed enhancements, boasting a 40x
acceleration in the training phase and a 15x improvement in inference time
compared to GNN and WL models. This not only underscores the efficacy of the
HDC-based method, but also highlights its potential for expedited and
resource-efficient graph learning.

我们的研究引入了一种创新的基于超维计算的图学习方法，与图神经网络和 Weisfieler-Lehman 图核心相比，在癌细胞识别等应用领域取得了可比拟的结果，并且在训练和推断阶段实现了显著的加速和资源效率的提升。