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

计算机视觉中的深度学习模型是高度专门化的自然成像领域。知识设计在图像类型选择方面一直受到青睐，但对于诸如计算病理学（即医学成像领域的一种变体）等新来者却做得很少。在计算病理学中，大多数图像表示模型都是计算机视觉领域的副产品，在大多数情况下，为了应用而进行了微小的调整。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，正因为如此，这导致了诸如在病理中处理大数据时缺乏数值效率、可解释性和人工智能模型用于表示的适用性等问题。我的长期目标是改变计算病理学的人工智能模型设计范式，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：在深度训练中为模型参数的有效和可解释的编码制定泛化措施。我们将设计先进的度量方法来编码训练参数，并整合这些度量方法来设计超参数优化算法。2. 开发知识聚合模型，以受益于计算病理学中的多源领域数据，这些数据在数字上高效、紧凑、隐私保护和可解释。我们将合并来自联合学习、知识蒸馏和泛化度量的进展来设计聚合模型。3. 开发用于计算病理学的深度神经网络架构优化设计的自动搜索算法。我们将融合多源数据的概化度量、知识聚合等方面的进展，为图像表示设计最佳的单元拓扑。4. 开发有监督的多标签对比学习损失函数，该函数高效、可推广、可解释病理图像表示。我们将通过为损失景观设计建模多模态概率距离来设计一个端到端的监督分类器。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。这个全面的研究计划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一个高度原创的范式转变，并为计算机视觉和医学成像应用程序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，以便为更好地设计图像表示的优化算法和模型架构提供决定性的行动。

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.