End-to-End eXplainable Model Designs in Deep Learning for Computational Pathology

计算病理学深度学习中的端到端可解释模型设计

• 批准号: RGPIN-2022-05378
• 负责人: Hosseini, Mahdi
• 金额: $ 2.11万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752123
• 关键词: End; eXplainable; Model; Designs; Deep

Deep learning models in computer vision are highly specialized for natural imaging domain. The knowledge design has been favored for such image type selection, but little is done for new arrivals such as computational pathology (i.e. a variant of medical imaging domain). Most models for image representation in computational pathology are the bi-products from computer vision domain-at most minor adjustments are made for application. Such blind transfer of model architectures and optimization algorithms are not necessarily the best practice for computational pathology. There are key differences between natural images and pathology images and because of that, this leads to issues such as the lack of numerical efficiency for handling bigdata in pathology, eXplainability, and suitability of AI models for representation. My long-term objective is to shift the paradigm of AI model designs for computational pathology, such that it enables engineering-science developers to bridge the existing gaps from end-to-end viewpoint. My short-term objectives are: 1. Develop generalization measures in deep training for effective and eXplainable encoding of model parameters. We will design advanced metric measures for encode training parameters and consolidate the measures to design hyper-parameter optimization algorithms. 2. Develop knowledge aggregation models to benefit from multiple-source domain data in computational pathology that are numerically efficient, compact, privacy-preserved, and eXplainable. We will merge advances from federated-learning, knowledge distillation, and generalization measures to design aggregation models. 3. Develop automated searching algorithms for optimum design of deep neural network architectures for computational pathology. We will merge advances from generalization measure, knowledge aggregation from multi-source data for designing optimum cell topology for image representation. 4. Develop supervised multi-label contrastive learning loss-functions that are efficient, generalizable, and explainable for pathology image representation. We will design an end-to-end supervised classifiers by modeling multi-modal probabilistic distances for loss-landscape designs. We will evaluate each of our objectives across many experiments and compare our solutions to existing model-centric and data-centric approach designs in the literature as well as considering EDI factors. This comprehensive research program bridges the existing gap for theoretical advancements in deep learning designs for computational pathology. It creates a highly original paradigm shift in designing deep learning models and contributes to groundbreaking advances in both computer vision and medical imaging application designs in general. This could be considered as the stepping-stone in advanced deep-learning problems to derive decisive actions for better design of optimization algorithms and model architectures for image representation.

计算机视觉中的深度学习模型高度专门用于自然成像领域。这种图像类型的选择的知识设计已被青睐，但很少做的新来港定居者，如计算病理学（即医学成像领域的一个变种）。计算病理学中的图像表示模型大多数是计算机视觉领域的副产品，最多是为了应用而做一些微小的调整。这种模型架构和优化算法的盲目转移不一定是计算病理学的最佳实践。自然图像和病理图像之间存在关键差异，因此，这导致了诸如处理病理学中的大数据缺乏数值效率，可解释性以及AI模型表示的适用性等问题。我的长期目标是改变计算病理学AI模型设计的范式，使工程科学开发人员能够从端到端的角度弥合现有的差距。我的短期目标是：1。在深度训练中开发泛化措施，以实现模型参数的有效和可解释的编码。我们将设计用于编码训练参数的高级度量措施，并巩固设计超参数优化算法的措施。2.开发知识聚合模型，以受益于计算病理学中的多源域数据，这些数据在数值上是高效的，紧凑的，隐私保护的，并且可解释的。我们将融合联邦学习，知识蒸馏和泛化措施的进步，以设计聚合模型。3.开发自动搜索算法，用于计算病理学的深度神经网络架构的优化设计。我们将合并的进步，从泛化措施，知识聚合从多源数据设计最佳的细胞拓扑结构的图像表示。4.开发有监督的多标签对比学习损失函数，这些函数对于病理图像表示是有效的，可推广的和可解释的。我们将设计一个端到端的监督分类器，通过建模多模态概率距离损失景观设计。我们将在许多实验中评估我们的每个目标，并将我们的解决方案与文献中现有的以模型为中心和以数据为中心的方法设计进行比较，同时考虑EDI因素。这个综合性的研究项目弥合了计算病理学深度学习设计理论进步的现有差距。它在设计深度学习模型方面创造了高度原创的范式转变，并为计算机视觉和医学成像应用设计的突破性进展做出了贡献。这可以被认为是高级深度学习问题的垫脚石，以获得更好的优化算法和图像表示模型架构设计的决定性行动。