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He Liu

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4 papers

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AAAI Conference 2026 Conference Paper

MIRA: Evaluating Multimodal AI on Complex Clinical Reasoning in Interventional Radiology

Jingxiong Li
Chenglu Zhu
Sunyi Zheng
Yuxuan Sun
Yifei Wang
He Liu
Yunlong Zhang
Yixuan Si

We present MIRA (Multimodal Interventional RAdiology evaluation), a comprehensive benchmark for evaluating large multimodal models in expert-level interventional radiology tasks requiring specialized domain knowledge and advanced visual reasoning capabilities. Unlike existing medical benchmarks that primarily provide binary labels without contextual depth, MIRA offers diverse question formats, including open-ended, closed-ended, single-choice, and multiple-choice categories, each accompanied by detailed expert-validated explanations. The benchmark incorporates approximately 184K high-quality medical images spanning multiple imaging modalities with 1.2M meticulously generated question-answer pairs across various anatomical regions. These pairs were created through a sophisticated cascade methodology involving expert interventional radiologists at both the data collection and validation stages. Our comprehensive evaluation, encompassing zero-shot testing and fine-tuning experiments of large multimodal models, revealing significant performance gaps between AI systems and human specialists. Fine-tuning experiments demonstrate substantial improvements, with models achieving up to 0.80 accuracy on single-choice questions. MIRA establishes a challenging benchmark that suggests promising directions for developing specialized clinical AI systems for interventional radiology.

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EAAI Journal 2025 Journal Article

Multi-modal multi-level feature representation learning for flow pattern identification of oil-water two-phase flow

Weihang Kong
Yaohan Chi
He Liu
Hongbao Tang
He Li

To tackle the difficulty of the traditional experimental methods in real-time monitoring and identification of the flow process, this paper introduces a novel flow pattern identification method of the vertical oil-water two-phase flow based on multi-modal multi-level feature representation. The one-dimensional electromagnetic signals are encoded into two-dimensional feature spaces to explore their structural complexity, evolutionary probability laws and nonlinear characteristics in multimodal domain, thereby generating a multi-modal representation of the electromagnetic signals. Subsequently, a multi-modal multi-level feature fusion network is developed for flow pattern identification network, which flexibly leverages effective information across different modalities and levels, thereby enhancing the identifying accuracy. Experimental results demonstrate that the proposed method achieves high accuracy on the constructed multi-modal dataset, proving its feasibility and effectiveness in identifying the flow pattern of the oil-water two-phase flow in vertical pipes.

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IJCAI Conference 2021 Conference Paper

GM-MLIC: Graph Matching based Multi-Label Image Classification

Yanan Wu
He Liu
Songhe Feng
Yi Jin
Gengyu Lyu
Zizhang Wu

Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as a instance-label matching selection problem. To model such problem, we propose a novel deep learning framework named Graph Matching based Multi-Label Image Classification (GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its excellent capability of excavating the instance and label relationship. Specifically, we first construct an instance spatial graph and a label semantic graph respectively, and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.

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AAAI Conference 2015 Conference Paper

Multi-Document Summarization Based on Two-Level Sparse Representation Model

He Liu
Hongliang Yu
Zhi-Hong Deng

Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time. In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it. Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.

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