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

用于医学语义分割的跨级卷积变压器和对抗性多任务学习

• 批准号: 2722537
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2722537
• 关键词: Cross; level; Convolutional; Transformer; Adversarial

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.

该项目计划研究用于医学语义细分（MSS）的跨层次卷积变压器和对抗性多任务学习。 MSS标签图像的每个像素的目标，具有相应的类别以提供分割图的代表类别。尽管基于深度学习的方法已经在MSS中实现了最先进的性能，但他们仍然很难在复杂的环境中实现细粒度的细分图，这禁止其对现实世界应用程序的实施。解决此问题。我计划探索一些有希望的MSS算法。首先，我将专注于通过提出具有跨层相互作用的卷积变压器来改善UNET的跳过连接。其次，我的目标是通过少量注释引起的模型性能的束缚，通过具有对抗性多任务学习的共享私有体系结构，以学习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：首先，该项目中研究的方法将在复杂的场景中提高MSS在复杂的场景中的强大潜在的多数范围/多数范围的数据范围/范围内的数据范围/范围内的数据范围/范围不超强。其次，由于共享私人的机制和对抗性学习，该项目将为MSS开发一个广义而启发性的结构。它可以用于具有异质输入的多任务，只需进行一些修改。iams和Ibsigivesthis项目旨在解决两个关键的挑战，以提高MSS在复杂的手术环境中的性能和可用性，挑战1：如何有效地提取高质量的功能并根据UNTET的范围来提取所有信息？由于不同层中语义差距的问题，途径是有效的。 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多模式数据，未标记的数据，甚至来自其他任务以改善模型性能的数据？由于数据和注释都昂贵，因此精心贴标签的数据集的稀缺性成为基于DL的MSS的不可避免的限制。以前的方法更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构建统一体系结构，无论其类型如何。该项目将为MSS提出一个新型的跨卷卷积变压器，以改善UNET的跳过连接过程。本项目将提出一个具有多个编码器和解码器的新型共享私人网络，以使对对抗性多任务学习以操作其他模态的数据或量表的表征，从而在各个模量表中进行了调查，以启用了各个模量/量表。