Active and continual learning strategies for deep learning assisted interactive segmentation of new databases

PHD项目的目标：开发交互式深度学习方法来不断分割图像数据库，对于这些数据库，以前没有注释的训练数据库存在。设计主动学习策略，以即时检索其手动分割将是持续学习的最大信息量的案例创建支持加速采用人工智能的注释工具。项目描述/背景：机器学习和人工智能的当代进步允许开发工具，可以帮助临床医生开发和量化包括图像、文本报告和遗传信息在内的临床数据。最先进的算法正在变得足够成熟，当提供足够高质量的训练数据并应用于良好控制的临床研究和试验时，足以提供自动化分析[1]，[2]。然而，很明显，制作手动体素准确的医学图像分割标签是乏味的、耗时的和昂贵的，因为它通常需要深厚的放射学专业知识。数据标注通常是基于特定应用的深度学习图像分割解决方案开发的速率限制因素。在本项目中，我们将重点设计机器学习方法来辅助和加速跨数据库的感兴趣结构的手动分割，可能从头开始。调整深度学习以支持新的应用程序，同时减少为培训目的收集和注释数据集所需的负担，仍然是一个活跃的研究领域[7]。本主题分享了域适应方面的挑战[3]，例如，当新一代扫描仪推出时，试图限制所需的新注释量。天真地将预先训练的模型应用于可能与用于获取训练模型所在的训练数据集的成像源稍有不同的成像源，实际上往往会导致显著的失败。通常需要新的注释来自信地弥合领域缺口并验证领域适应技术的性能。在这种情况下，临床医生通常需要完全手动或通用的交互方法来描绘感兴趣的结构。交互式深度学习方法正在兴起，它将嵌入在以前患者回顾数据中的丰富先验知识与临床医生提供的尽可能稀疏的注释结合在一起。然而，这些技术目前在用于新病例时并没有继续学习和改进。同时，算法已经被设计成利用跨数据集注释的弱标签来训练深度神经网络[6]。同样，这些方法需要手动分割地面真实数据用于验证目的，并且不会因为出现新的案例而学习。本项目将考虑在潜在仅有很少先验知识的情况下逐渐注释图像分割数据集的问题。这是一个非常及时的研究问题，到目前为止，很少有机器学习作品考虑过这个问题。

Aim of the PhD Porject:Develop interactive deep learning approaches to continually segment databases of images for which no previously annotated training databases existDesign active learning strategies to retrieve on-the-fly, cases whose manual segmentation will be the most informative for continual learningCreate annotation tools that support accelerated adoption of AI for new applications.Project Description / Background:Contemporary progresses in machine learning and artificial intelligence have permitted the development of tools that can assist clinicians in exploiting and quantifying clinical data including images, textual reports and genetic information. State-of-the-art algorithms are becoming mature enough to provide automated analysis when provided with enough high-quality training data and when applied to well-controlled clinical studies and trials [1], [2]. It is clear though that producing manual voxel-accurate medical image segmentation labels is tedious, time-consuming and costly as it usually requires profound radiological expertise. Data annotation is often a rate-limiting factor for the development of application-specific deep learning based image segmentation solutions.In this project, we will focus on designing machine learning approaches to assist and accelerate the manual segmentation of structures of interest across a database, potentially starting from scratch. Adapting deep learning to support new applications while reducing the burden required to collect and annotate datasets for training purposes remains an active research area [7]. This topic shares challenges with domain adaption [3], for example when trying to limit the amount of new annotations required when new generations of scanners are being rolled out. Naively applying a pre-trained model to an imaging source that may slightly differ from the one used to acquire the training data set on which the model was trained indeed often results in dramatic failures. New annotations are often required to confidently bridge the domain gap and validate the performance of domain adaptation techniques.In such cases, clinicians are typically left with fully manual or generic interactive methods to delineate structures of interest. Interactive deep learning methodologies are emerging to combine rich prior knowledge embedded in retrospective data from previous patients with as-sparse-as-possible annotations provided by clinicians [4], [5]. Yet, these techniques do currently not continue to learn and improve when being used for new cases. Concurrently, algorithms have been designed to exploit weak labels annotated across a data set to train deep neural networks [6]. Again, these methods require manually segmented ground truth for validation purposes and do not learn for being presented with new cases.This project will consider the problem of gradually annotating an image segmentation dataset with potentially only very little prior knowledge. This is a timely research question which very few machine learning works have considered so far.