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Yanyu Liu

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4 papers

1 author row

AIJ Journal 2025 Journal Article

Active legibility in multiagent reinforcement learning

Yanyu Liu
Yinghui Pan
Yifeng Zeng
Biyang Ma
Prashant Doshi

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimise their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms. • We propose the multiagent active legibility framework to develop legible plans in MARL. • We propose the legibility reward shaping technique and prove its correctness. • We design multiple problem domains to showcase the plan recognition and legibility.

Details DOI

JBHI Journal 2025 Journal Article

Pathological Image Segmentation of Breast Cancer via Template Matching

Kaixiang Yan
Yanyu Liu
Junjie Li
Jinde Cao
Dongming Zhou

Accurate pathological image segmentation is crucial for the clinical diagnosis of breast cancer. However, existing methods of pathological segmentation face challenges due to the variability and complexity of breast cancer on pathological images. To address these issues, we propose a novel segmentaion network called template-matching pathological segmentation network. Our method incorporates an innovative template matching strategy inspired by the diagnostic process of pathologists. The template matching strategy is to utilize visual transformer to establish a correlative relationship between cancer lesions and corresponding templates. To improve feature utilization of pathological images, PSVTNet introduces detailed information attention and information entropy attention. Detailed information attention aims to exploit detailed information by serving as the path connecting shallow-layer and deep-layer features. Meanwhile, information entropy attention can redistribute feature weights to high-entropy regions according to the information-entropy attention map. Additionally, this work releases a comprehensive pathological dataset that comprises labeled pathological images. These images are collected from breast and stomach cancers with hematoxylin&eosin and human epidermal growth factor receptor-2 staining. Extensive experiments demonstrate that PSVTNet significantly outperforms state-of-the-art methods on pathologic images of breast cancer, but can also process pathologic images of stomach cancer carrying with same diagnosed features as the breast cancer.

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JBHI Journal 2023 Journal Article

An Improved Hybrid Network With a Transformer Module for Medical Image Fusion

Yanyu Liu
Yongsheng Zang
Dongming Zhou
Jinde Cao
Rencan Nie
Ruichao Hou
Zhaisheng Ding
Jiatian Mei

Medical image fusion technology is an essential component of computer-aided diagnosis, which aims to extract useful cross-modality cues from raw signals to generate high-quality fused images. Many advanced methods focus on designing fusion rules, but there is still room for improvement in cross-modal information extraction. To this end, we propose a novel encoder-decoder architecture with three technical novelties. First, we divide the medical images into two attributes, namely pixel intensity distribution attributes and texture attributes, and thus design two self-reconstruction tasks to mine as many specific features as possible. Second, we propose a hybrid network combining a CNN and a transformer module to model both long-range and short-range dependencies. Moreover, we construct a self-adaptive weight fusion rule that automatically measures salient features. Extensive experiments on a public medical image dataset and other multimodal datasets show that the proposed method achieves satisfactory performance.

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AAMAS Conference 2023 Conference Paper

Improvement and Evaluation of the Policy Legibility in Reinforcement Learning

Yanyu Liu
Yifeng Zeng
Biyang Ma
Yinghui Pan
Huifan Gao
Xiaohan Huang

When we work with intelligent agents, such as fighting a battle with other agents in computer games, it is difficult to achieve seamless collaboration if we can’t figure out what the agents are doing. Especially in a complex problem domain, the agents are well trained and their actions could be too sophisticated to be comprehended by humans. In this article, we propose a novel reward shaping mechanism to improve the legibility of reinforcement learning that is used to train agents’ policies. More importantly, we develop an interactive system to seek for users’ evaluation of the policy legibility and show performance of the new learning approach.

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