Towards Effective and Interpretable Deep Learning Applications for Microscopic Medical Imaging

深度学习迅速发展到解决计算机视觉等许多领域的难题，特别是在有大量数据可用的问题上。然而，对于许多医疗应用，可用的数据往往是小型到中型的数据集。此外，这些数据集通常是不完整的，包含输入错误，并且在目标分类方面不平衡。对于医学成像数据，除了解剖结构的基本复杂性之外，还存在收集过程中的噪声和成像伪影的存在的挑战。我们研究的长期目标是开发强大、可访问、快速、透明和准确的计算机辅助医疗诊断和分级工具，为临床医生提供自动独立的第二意见，帮助避免错误/疏忽。我们提案中建议的研究旨在引入或适应新的深度学习方法，以解决与中小型医疗数据的分类、分割和可解释性相关的问题。所考虑的分类问题主要与计算机辅助诊断(CAD)系统的发展有关，特别是对于乳腺癌细针活检的细胞学图像，我们寻求提高准确性和稳健性，以实现临床可接受的性能。我们将继续我们的研究，利用疾病的进展来提高诊断水平。为了创建用于深度学习训练的增强数据，我们将研究通过适应我们最近在生成性对抗网络方面的工作来创建图像和医学图像序列。对于有噪声的医学数据，我们将寻求在存在此类数据的情况下改进训练，使用局部固有维度来局部评估训练的模型。我们最近在持续学习方面的工作将扩展到提高CAD系统的可靠性，以允许在线学习数据分布的变化。最后，我们将继续我们在元学习方面的工作，将最近在无监督少镜头元学习方面的工作扩展到放射应用。我们将继续改进光学显微图像的核分割算法。除了尝试新的更深层次的体系结构外，我们还将探索用于分割医学图像的新的损失函数的设计，探索超越优化方法的技术的发展，如深度监督，以及算法学习速率调度器的开发。对于用于恶性肿瘤诊断或分级的深度学习模型，这些系统被批准用于临床的一个主要困难是缺乏对这些模型做出的决定的可解释性。为了解释深度学习模型做出的决定，我们正在研究手工制作的特征和深度学习模型特征之间的相关性，从过去患者中寻找相似图像的图像检索策略，以及反事实视觉解释的生成。

Deep learning has quickly evolved to solve difficult problems in many domains such as computer vision, particularly for problems where large amounts of data are available. For many medical applications, though, the data available tend to be small to medium size data sets. Further, these data sets typically are incomplete, contain entry errors, and unbalanced in terms of target classifications. For medical imaging data, beyond the basic complex nature of anatomical structures, there are challenges of noise from the collection process and the presence of imaging artifacts. The long-term objective of our research is to develop robust, accessible, rapid, transparent and accurate computer-aided medical diagnosis and grading tools to provide an automatic independent second opinion, to help avoid errors/oversights, to clinicians. The proposed research in our proposal aims to introduce or adapt new deep learning approaches to solve problems related to classification, segmentation, and interpretability for small- or medium-scale medical data. The classification problems considered are primarily related to the development of Computer Aided Diagnosis (CAD) systems, particularly for cytological images of fine needle biopsies for breast cancer, where we seek to improve accuracy and robustness to achieve clinically acceptable performance. We will continue our research on leveraging the progression of a disease over time to improve diagnosis. To create augmented data for deep learning training, we will study the creation of images and sequences of medical images by adapting our recent work on generative adversarial networks. For noisy medical data, we will seek to improve training in the presence of such data using local intrinsic dimensionality to locally evaluate the trained model. Our recent work on Continual Learning will be extended to improve the reliability of CAD systems to allow online learning of shifts in data distributions. Finally, we will our continue work on Meta-Learning by extending recent work on unsupervised Few-Shot Meta-Learning to radiomic applications. We will continue our work on improved nuclear segmentation algorithms for optical microscopic images. In addition to trying new deeper architectures, we will also explore the design of new loss functions for segmenting medical images, exploring the development of techniques beyond optimization approaches such as deep supervision, and the development of algorithmic learning rate schedulers. For deep learning models for malignancy diagnosis or grading, one major difficulty in these systems being approved for clinical use is the lack of interpretability of the decisions made by these models. To interpret the decisions made by deep learning models, we are studying correlations between handcrafted features and deep learning model features, the image retrieval strategies for finding similar images from past patients, and the generation of counterfactual visual explanations.