Cross-level Convolutional Transformer and Adversarial Multi-task Learning for Medical Semantic Segmentation

该项目计划研究用于医学语义分割（MSS）的跨级别卷积变换器和对抗性多任务学习。MSS的目标是为图像的每个像素标记一个相应的类别，以提供分割图。虽然基于深度学习的方法在MSS中已经达到了最先进的性能，但它们仍然难以在复杂环境中实现细粒度的分割图，这阻碍了它们在现实世界中的应用。我计划为MSS探索一些有前途的算法。首先，我将通过提出一个具有跨级别交互的卷积Transformer来专注于改进UNet的跳过连接。第二，我将致力于打破由于标注数量少而导致的模型性能的桎梏，通过一个带有对抗性多任务学习的共享私有架构来使用尽可能多的额外数据。该项目的潜在影响主要包括两个方面：第一、本项目所研究的方法将提高MSS在复杂场景中的模型性能，并有很强的潜力扩大MSS的适用性。多模态/未标记/多任务数据。其次，该项目将开发一个通用的和指导性的结构MSS感谢共享的私人机制和对抗性学习。它可以用于多个任务与异构的输入，只有几个modified.Aims和ObjectivesThis项目旨在解决两个关键的挑战，以提高MSS的性能和可用性在复杂的外科环境。挑战1：如何提取高质量的功能，并有效地融合它们？基于UNet的最新MSS方法由于以下两个方面而未能从全尺度探索足够的信息：a）由于不同层中的语义间隙问题，并非所有连接路径都是有效的。那些冗余和不相关的连接增加了网络的训练难度，甚至有些连接会破坏性能。B）跳过贡献的最佳组合在不同的数据集之间是不同的，这取决于分割对象的尺度和外观。为了解决上述问题，我考虑用Transformers替换普通的跳过连接路径，以捕获非本地特征并有效地执行交叉连接。挑战2：如何利用多模态数据、未标记数据，甚至是来自其他任务的数据来提高模型性能？在基于DL的MSS中，缺乏仔细标记的数据集成为一个不可避免的限制，因为数据和注释的获取都很昂贵。以前的方法很少注意利用不同类型的外部数据。因此，我考虑使用对抗性多任务学习来为额外的数据构建统一的架构，无论其类型如何。本项目将为MSS提出一种新型的跨级别卷积Transformer，以改善UNet的跳过连接过程。本项目将为MSS提出一种新型的具有多个编码器和解码器的共享专用网络，以利用对抗性多任务学习来处理额外的数据或任务。由于手术图像数据通常具有多模态/尺度和复杂场景的特征，在此基础上的研究对复杂MSS的研究具有重要的意义。

This project plans to study cross-level convolutional Transformers and adversarial multi-task learning for medical semantic segmentation (MSS). The goal of MSS labels each pixel of an image with a corresponding class of what is being represented to provide the segmentation maps. Though deep learning-based methods have achieved state-of-the-art performance in MSS, they still struggle to achieve fine-grained segmentation maps in complex environments, which prohibits their implementation to real-world applications.To handle this problem. I plan to explore some promising algorithms for MSS. First, I will focus on improving skip connections of UNet by proposing a convolutional Transformer with cross-level interaction. Second, I will aim to break the shackle of model performance caused by the small number of annotations, through a shared-private architecture with adversarial multi-task learning to use as much additional data as possible.The potential impact of this project mainly includes two aspects: First, the methods studied in this project will improve the model performance of MSS in complex scenes and have strong potential to broaden the applicability of multi-modal/unlabeled/multi-task data. Second, this project will develop a generalized and instructive structure for MSS thanks to the shared-private mechanism and adversarial learning. It could be used for multi-tasks with heterogeneous inputs, with only a few modifications.Aims and ObjectivesThis project aims to address two key challenges to improve the performance and usability of MSS in complex surgical environments.Challenge 1: How to extract high-quality features and fuse them effectively?The latest MSS methods based on UNet fail to explore sufficient information from full scales due to the following two aspects:a) Not all connection pathways are effective due to the issue of semantic gaps in different layers. Those redundant and irrelevant connections increase the training difficulty of the network, even some can undermine the performance.b) The optimal combination of skip contributions is varied among different datasets, which depends on the scales and appearance of segmentation objects.To address the above problems, I consider replacing vanilla skip-connection pathways with Transformers to capture non-local features and perform effectively cross-level feature fusion.Challenge 2: How to utilize multi-modal data, unlabeled data, or even data from other tasks to improve the model performance?The scarcity of carefully-labelled datasets becomes an unavoidable limitation in DL-based MSS as both data and annotations are expensive to acquire. Previous methods pay less attention to utilizing different types of external data. Therefore, I consider using adversarial multi-task learning to build a uniform architecture for additional data, regardless of its type. This project will propose a novel cross-level convolutional Transformer for MSS to improve the skip-connection process of UNet.This project will propose a novel shared-private network with multiple encoders and decoders for MSS to utilize adversarial multi-task learning to handle additional data or tasks.As surgical image data are generally characterized by multiple modalities/scales and complex scenes, the research based on it could shed light on complex MSS.