Author name cluster

Can Zhao

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss

Can Zhao
Pengfei Guo
Dong Yang
Yufan He
Yucheng Tang
Benjamin Simon
Mason Belue
Stephanie Harmon

Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their strengths, many existing methods struggle with (1) limited generalizability, only working for specific body regions or voxel spacings, (2) slow inference, which is a common issue for diffusion models, and (3) weak alignment with input conditions, which is a critical issue for medical imaging. MAISI, a previously proposed framework, addresses generalizability issues but still suffers from slow inference and limited condition consistency. In this work, we present MAISI-v2, the first accelerated 3D medical image synthesis framework that integrates rectified flow to enable fast and high-quality generation. To further enhance condition fidelity, we introduce a novel region-specific contrastive loss to improve sensitivity to the region of interest. Our experiments show that MAISI-v2 can achieve state-of-the-art image quality with 33× acceleration for latent diffusion models. We also conducted a downstream segmentation experiment to show that the synthetic images can be used for data augmentation. We release our code, training details, model weights, and a GUI demo to facilitate reproducibility and promote further development within the community.

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AAAI Conference 2026 Conference Paper

NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation

Maoqi Liu
Quan Fang
Yuhao Wu
Can Zhao
Yang Yang
Kaiquan Cai

Accurate interpretation of Notices To Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to "Shallow Parsing," failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as "Deep Parsing," a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a Large Language Model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a crucial closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state-of-the-art on the task of structured NOTAM interpretation.

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TMLR Journal 2025 Journal Article

Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation

Chun-Yin Huang
Ruinan Jin
Can Zhao
Daguang Xu
Xiaoxiao Li

While Federated Learning (FL) is gaining popularity for training machine learning models in a decentralized fashion, numerous challenges persist, such as asynchronization, computational expenses, data heterogeneity, and gradient and membership privacy attacks. Lately, dataset distillation has emerged as a promising solution for addressing the aforementioned challenges by generating a compact synthetic dataset that preserves a model's training efficacy. However, we discover that using distilled local datasets can amplify the heterogeneity issue in FL. To address this, we propose Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation (FedLGD), where we seamlessly integrate dataset distillation algorithms into FL pipeline and train FL using a smaller synthetic dataset (referred as virtual data). Specifically, to harmonize the domain shifts, we propose iterative distribution matching to inpaint global information to *local virtual data* and use federated gradient matching to distill *global virtual data* that serve as anchor points to rectify heterogeneous local training, without compromising data privacy. We experiment on both benchmark and real-world datasets that contain heterogeneous data from different sources, and further scale up to an FL scenario that contains a large number of clients with heterogeneous and class-imbalanced data. Our method outperforms *state-of-the-art* heterogeneous FL algorithms under various settings.

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ECAI Conference 2025 Conference Paper

RANA: Robust Active Learning for Noisy Network Alignment

Yixuan Nan
Xixun Lin
Yanmin Shang
Zhuofan Li
Can Zhao
Yanan Cao 0001

Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. RANA effectively tackles both structure noise and label noise while addressing the sparsity of anchor link annotations, which can improve the robustness of network alignment models. Specifically, RANA introduces the proposed Noise-aware Selection Module and the Label Denoising Module to address structural noise and labeling noise, respectively. In the first module, we design a noise-aware maximization objective to select node pairs, incorporating a cleanliness score to address structural noise. In the second module, we propose a novel multi-source fusion denoising strategy that leverages model and twin node pairs labeling to provide more accurate labels for node pairs. Empirical results on three real-world datasets demonstrate that RANA outperforms state-of-the-art active learning-based methods in alignment accuracy. Our code is available at https: //github. com/YXNan0110/RANA.

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ICRA Conference 2023 Conference Paper

In-situ Mechanical Calibration for Vision-based Tactile Sensors

Can Zhao
Jieji Ren
Hexi Yu
Daolin Ma

This paper proposes a novel approach to conduct routine calibration for the changing mechanical parameters over time of a vision-based tactile sensor, without disassembling its overall structure, i. e. , in-situ mechanical calibration. Calibration for mechanical parameters, Young's modulus and Poisson's ratio, of a tactile sensor's sensing elastomer, is crucial for its force perception capabilities. However, there are few methods that can retrieve values of these parameters both accurately and conveniently. To address this problem, we propose an in-situ approach to calibrate mechanical parameters other than the verbose traditional evaluation process. This method incorporates the deformation sensing capability of the sensor, the accurate force sensing capability of a force/torque sensor, and most importantly, the deformation-force relation-ship for an indentation with embedded mechanical parameters of the elastomers. We also present the indentation test setup and the complete pipeline to extract Young's modulus and Poisson's ratio from experimental results. We validate the method by comparing the indentation depths simulated through finite element analysis (FEA) using the cali-brated parameters with the indentation depths measured in real experiments. Furthermore, superior contact force distribution can be achieved with the accurate mechanical parameters. The proposed method provides the theoretical basis for accurate, lifelong routine calibration, whether weekly or even daily, which can enhance the applications of tactile sensors in real manipulation scenarios.

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