Correcting biases in deep learning models

项目摘要/摘要 深度学习已被广泛应用于所有生命科学中来构建预测模型。然而，它 依赖于训练样本是独立且同分布的假设。这经常是 在生命科学中被违反，其中数据通过来自同一样本(患者，细胞， 组织)、由同一观察者或在同一地点。这导致相关数据的集群(随机效果)，以及 当模型适合这样的数据时，模型参数可能会严重偏差，从而导致类型I和II 错误。对DL模型中的这种依赖关系进行适当的核算尚未得到解决。这项提议的目的是 是开发适当的DL修改，以分别对全局固定效果和随机效果进行建模， 提高与人类疾病相关的准确、无偏见预测的模型可解释性和性能。 我们的建议是基于一个新的、模型不可知的框架，将传统的动态链接库模型转换为适当的模型 混合效应DL(MEDL)模型。这为统计线性混合效应模型提供了能力，包括 将集群不变的固定效果从集群特定的随机效果中分离出来，同时保持 学习数据驱动的非线性关联。核心前提是适当的MEDL模型1)更多 对混乱的影响具有弹性，更注重真实的预测特征，2)可以捕获、量化和 可视化随机效果以增强可解释性，以及3)获得对新聚类的更好的泛化。我们 建议将MEDL纳入三种最重要的DL模型类型，包括密集前馈 神经网络(DFNN)、卷积神经网络(CNN)和自动编码器。我们的初步结果 在准确性和可解释性方面，展示了MEDL相对于传统DL的多个优势。MEDL 优于以前的集群数据方法，包括：领域对抗性模型、元学习和 将聚类成员资格作为输入协变量纳入。我们开发了ME-DFNN来预测从 从表格数据到阿尔茨海默病(AD)的轻度认知障碍，从MRI诊断AD的ME-CNN， 以及ME自动编码器，用于对活细胞图像进行压缩和分类。在这些测试案例中，MEDL模型是 已知的混淆特征和真实特征之间的最具区分性；他们能够量化或可视化 随机效应，并在训练过程中看到和看不到的集群上的表现优于其他模型。这项建议 通过外部验证，进一步开发了处理复杂架构和分层效果的方法， 通过这些目标：1)开发用于分类和回归的ME-DFNN。2)开发3D ME-CNN和多个ME-CNN 用于医学图像分类的3DME-CNN模型。3)开发卷积和向量ME自动编码器，用于 图像和组学数据。我们描述了创新地结合对抗性分类器来约束基地 学习固定效果的模型、贝叶斯随机效果子网络和应用随机效果的方法 到看不见的星团。所有这些解决方案都将以开源软件的形式发布，以改进现有的DL模型 最终支持精准生物医学用于人类疾病的研究和治疗。

Project Summary/Abstract Deep learning (DL) has been widely applied across all life sciences to construct predictive models. However, it relies on the assumption that training samples are independent and identically distributed. This is frequently violated in the life sciences, where data is “grouped” by measurements from the same sample (patient, cell, tissue), by the same observer, or at the same site. This leads to clusters of correlated data (random effects), and when the models are fit to such data, the model parameters can be severely biased, leading to type I and II errors. Proper accounting for such dependencies in DL models has gone unsolved. The objective of this proposal is to develop the appropriate DL modifications to separately model global fixed effects and random effects that increase model interpretability and performance for precise unbiased predictions related to human disease. Our proposal is based on a novel, model-agnostic framework to transform conventional DL models into proper mixed effects DL (MEDL) models. This affords capabilities of statistical linear mixed effects models, including the separation of cluster-invariant fixed effects from cluster-specific random effects, while preserving the ability of DL to learn data-driven nonlinear associations. The core premise is that proper MEDL models 1) are more resilient to confounding effects and more attentive to true predictive features, 2) can capture, quantify, and visualize random effects to enhance interpretability, and 3) attain better generalization to new clusters. We propose to incorporate MEDL into three of the most important DL model types including dense feed-forward neural networks (DFNNs), convolutional neural networks (CNNs), and autoencoders. Our preliminary results demonstrate multiple advantages of MEDL over conventional DL in both accuracy and interpretability. MEDL outperforms previous clustered data approaches including: domain adversarial models, meta-learning, and the inclusion of cluster membership as an input covariate. We developed an ME-DFNN to predict conversion from mild cognitive impairment to Alzheimer’s Disease (AD) from tabular data, an ME-CNN to diagnose AD from MRI, and an ME-autoencoder to compress and classify live cell images. Across these test cases, MEDL models were the most discriminative between known confounded and real features; they were able to quantify or visualize the random effects and outperformed other models on clusters both seen and unseen during training. This proposal further develops the methods to handle complex architectures and hierarchical effects, with external validation, through these aims: 1) Develop ME-DFNNs for classification and regression. 2) Develop 3D ME-CNNs and multi- modal 3D ME-CNNs for medical image classification. 3) Develop convolutional and vector ME-autoencoders for image and omics data. We describe the innovative incorporation of an adversarial classifier to constrain the base model to learn fixed effects, a Bayesian random effects subnetwork, and an approach to apply random effects to unseen clusters. All these solutions will be released as open source software that improve existing DL models to ultimately support precision biomedicine for the study and treatment of human disease.