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

本项目计划研究用于医学语义分割(MSS)的跨级卷积变换和对抗性多任务学习。MSS的目标是用所表示的内容的对应类别来标记图像的每个像素，以提供分割图。尽管基于深度学习的方法在MSS中取得了最好的性能，但它们仍然难以在复杂环境中获得细粒度的分割地图，这阻碍了它们在实际应用中的实现。我计划为MSS探索一些有前途的算法。首先，我将通过提出一种具有跨级相互作用的卷积变压器来改进UNET的跳跃连接。第二，通过对抗性多任务学习的共享-私有体系结构，打破注释数量少对模型性能的束缚。本项目的潜在影响主要包括两个方面：第一，本项目研究的方法将提高MSS在复杂场景下的模型性能，并具有扩大多通道/无标签/多任务数据的适用性的强大潜力。其次，由于共享-私人机制和对抗性学习，该项目将为MSS开发一个通用的和有指导意义的结构。该项目旨在解决两个关键挑战，以提高复杂手术环境下MSS的性能和可用性。挑战1：如何提取高质量的特征并进行有效的融合？基于UNT的最新MSS方法由于以下两个方面的原因而无法从完整的尺度上挖掘足够的信息：a)由于不同层的语义差距问题，并不是所有的连接路径都是有效的。这些冗余和不相关的连接增加了网络的训练难度，甚至有些连接可能会影响网络的性能。b)跳跃贡献的最佳组合在不同的数据集上是不同的，这取决于分割对象的规模和外观。为了解决上述问题，我考虑用变形金刚代替普通的跳连路径来捕捉非局部特征并执行有效的跨层特征融合。挑战2：如何利用多模式数据、未标记数据甚至来自其他任务的数据来提高模型性能？由于数据和注释的获取都很昂贵，因此缺乏经过仔细标记的数据集成为基于DL的MSS中不可避免的限制。以前的方法对利用不同类型的外部数据关注较少。因此，我考虑使用对抗性多任务学习来为附加数据构建统一的体系结构，而不考虑其类型。本项目将提出一种新的跨级卷积转换器，用于改善UNET的跳跃连接过程。本项目将提出一种新颖的共享-专用网络，其中包括多个编解码器，用于MSS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式/尺度和复杂场景的特点，基于它的研究将有助于对复杂MSS的研究。

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.