Han Wu ε΄η€š

Ph.D. Student @ ShanghaiTech

I am a Ph.D. student at the School of Biomedical Engineering, ShanghaiTech University, advised by Prof. Dinggang Shen. I obtained my B.Eng. with honors from Wuhan University of Technology in 2022.

During the initial stage of my Ph.D. (Sept. 2022 - June 2024), I focused on digital dentistry, working closely with Prof. Zhiming Cui. Since 2024, my research interest has shifted toward computer-aided cardiovascular disease diagnosis and treatment. Currently, I am also a research intern at United Imaging Intelligence (UII), working with Dr. Dijia Wu.

Han Wu
πŸ₯• carrot

Ph.D. Student @ ShanghaiTech

Research Focus

My research focuses on developing intelligent, data-driven systems for computer-aided diagnosis and treatment in healthcare, with particular emphasis on cardiovascular and dental applications. My work centers on two core technical areas:

Label-Efficient Learning for Medical Imaging
Developing robust deep learning models that achieve clinical-grade performance with limited, weak, or partial annotations to address real-world data scarcity challenges.
VFM/VLM for Medical Image Analysis
Advancing Vision Foundation Models (VFMs) for medical image interpretation tasks (segmentation, landmark detection, etc.) and Vision-Language Models (VLMs) for comprehensive image understanding and clinical reasoning.
1class ContactInformationCard:
2 def __init__(self):
3 self.dept = "School of Biomedical Engineering, ShanghaiTech University"
4 self.lab = "IDEA Lab, BME Building 405"
5 self.email = "hanwu@shanghaitech.edu.cn"
6 self.phone = ""
7
8 def flipCard(self):
9 print("tap on the card to flip.")
10
11 def closeCard(self):
12 print("tap outside to close it.")

Han Wu

hanwu.website/

Education

ShanghaiTech University, Shanghai, China

Ph.D. in Computer Science

Sept. 2022 - Present

Supervisor: Prof. Dinggang Shen

Wuhan University of Technology, Wuhan, China

B.E. in Information Engineering

Sept. 2018 - June 2022

Outstanding Graduates (Postgraduate-Recommendation)

Experience

United Imaging Intelligence, Shanghai, China

Research Intern @ R&D

June 2025 - Present

Working with Dr. Dijia Wu.

News

Publications

* indicates equal contribution and + indicates corresponding author.

Cardiovascular

3
Semi-Supervised Landmark Tracking in Echocardiography Video via Spatial-Temporal Co-Training and Perception-Aware Attention
Semi-Supervised Landmark Tracking in Echocardiography Video via Spatial-Temporal Co-Training and Perception-Aware Attention
IEEE Transactions on Medical Imaging (IEEE TMI), 2026
Han Wu, Haoyuan Chen, Lin Zhou+, Qi Xu+, Zhiming Cui+, Dinggang Shen+
PDF
Abstract: Precise landmark annotation in cardiac ultrasound images is fundamental for quantitative cardiac health assessment. However, the time-intensive nature of manual annotation typically constrains clinicians to annotate only selected key-frames, limiting comprehensive temporal analysis capabilities. While recent automated landmark detection methods have demonstrated success for key-frame analysis, they fail to address the clinical need for continuous temporal evaluation across entire cardiac sequences. To bridge this gap, we present SemiEchoTracker, a novel semi-supervised framework that enables comprehensive landmark tracking throughout echocardiography sequences while requiring supervision only on key-frames. Our framework introduces three key innovations: 1) a co-training mechanism that enforces mutual consistency between spatial detection and temporal tracking, enabling accurate intermediate frame detection without additional annotations, 2) a guided DINOv2 pretraining strategy tailored for extracting fine-grained echocardiography-specific spatial features, and 3) a perception-aware spatial-temporal (PAST) attention module that efficiently captures inter- and intra-frame relationships in echocardiography videos. To the best of our knowledge, this work presents the first end-to-end approach for comprehensive landmark tracking in echocardiography sequences using only key-frame supervision. Extensive validation across multiple cardiac views demonstrates that our method achieves state-of-the-art performance in both detection and tracking tasks. ... See More
Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation
Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation
IEEE Transactions on Medical Imaging (IEEE TMI), 2025
Han Wu, Chen Wang, Zhiming Cui+
PDF Code Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation repository stats
Abstract: Semi-supervised learning has proven highly effective in tackling the challenge of limited labeled training data in medical image segmentation. In general, current approaches, which rely on intra-image pixel-wise consistency training via pseudo-labeling, overlook the consistency at more comprehensive semantic levels (e.g., object region) and suffer from severe discrepancy of extracted features resulting from an imbalanced number of labeled and unlabeled data. To overcome these limitations, we present a new Dual Cross-image Semantic Consistency (DuCiSC) learning framework, for semi-supervised medical image segmentation. Concretely, beyond enforcing pixel-wise semantic consistency, DuCiSC proposes dual paradigms to encourage region-level semantic consistency across: 1) labeled and unlabeled images; and 2) labeled and fused images, by explicitly aligning their prototypes. Relying on the dual paradigms, DuCiSC can effectively establish consistent cross-image semantics via prototype representations, thereby addressing the feature discrepancy issue. Moreover, we devise a novel self-aware confidence estimation strategy to accurately select reliable pseudo labels, allowing for exploiting the training dynamics of unlabeled data. Our DuCiSC method is extensively validated on four datasets, including two popular binary benchmarks in segmenting the left atrium and pancreas, a multi-class Automatic Cardiac Diagnosis Challenge dataset, and a challenging scenario of segmenting the inferior alveolar nerve that features complicated anatomical structures, showing superior segmentation results over previous state-of-the-art approaches. ... See More
A Semi-Supervised Knowledge Distillation Framework for Left Ventricle Segmentation and Landmark Detection in Echocardiograms
A Semi-Supervised Knowledge Distillation Framework for Left Ventricle Segmentation and Landmark Detection in Echocardiograms
MICCAI 2025
Haoyuan Chen, Yonghao Li, Long Yang, Han Wu, Lin Zhou, Kaicong Sun, Dinggang Shen+
PDF
Abstract: Left ventricular segmentation and landmark detection from echocardiograms are routine practices in clinical settings for comprehensive cardiovascular disease evaluation. Recently, deep learning-based models have been developed to interpret echocardiograms. However, existing methods face challenges in handling sparse annotations, limiting their clinical applicability. Additionally, their robustness can be significantly influenced by temporal inconsistency (i.e., abrupt prediction fluctuations between consecutive frames) and inter-task conflict (i.e., detected landmarks deviating from segmentation boundaries). To address these issues, we propose a novel semi-supervised framework that integrates: 1) a knowledge distillation method for generating pseudo labels of the numerous unlabeled frames to improve the performance; 2) a Task-aware Spatial-Temporal Network (TSTNet) along with consistency constraints that enhances robustness by enforcing temporal consistency across frames, and inter-task consistency between segmentation and landmark detection. Experimental results on two datasets (a public dataset with 1,000 subjects and a private dataset with 1,950 subjects) show that the proposed framework significantly outperforms the previous approaches. The source code and dataset will be publicly released upon paper acceptance. ... See More

Digital Dentistry

5
CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment
CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment
Medical Image Analysis (MedIA), 2025
Yulong Dou, Han Wu, Changjian Li, Chen Wang, Tong Yang, Min Zhu, Dinggang Shen, Zhiming Cui+
PDF Code CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment repository stats
Abstract: Orthodontics aims to rectify misaligned teeth (i.e., malocclusion), affecting both oral health and aesthetics. However, traditional methods for aligning teeth involve tedious and laborious manual procedures that heavily depend on the expertise of dentists, lead- ing to inefficient and prolonged treatment durations. Various automatic methods have been proposed to assist the less experienced dentists. Yet, most existing approaches lack clinical insight and often reduce the problem to directly mapping dental point clouds to transformation matrices. This over-simplification fails to capture the nu- anced requirements of orthodontic treatment, namely that effective alignment of mis- aligned teeth must adhere to specific clinical rules (e.g., Andrews' Six Keys & Six Elements). To bridge this gap, we propose CLIK-Diffusion, a CLInical Knowledge- informed Diffusion model for automatic tooth alignment. CLIK-Diffusion formulates the complex problem of tooth alignment as a more manageable landmark transformation problem, which is further refined into a landmark coordinate generation task. Specif- ically, we first detect landmarks for each tooth based on its specific category and then construct our CLIK-Diffusion to learn the distribution of normal occlusion. To encour- age the integration of essential clinical knowledge, CLIK-Diffusion proposes hierarchi- cal constraints from three paradigms: 1) dental-arch level: to constrain the arrangement of teeth from a global perspective, 2) inter-tooth level: to ensure tight contacts and avoid unnecessary collisions between neighboring teeth, and 3) individual-tooth level: to guarantee the correct orientation of each tooth. Ultimately, CLIK-Diffusion predicts the post-orthodontic landmarks that align with clinical knowledge. Rigid transforma- tions are computed based on the coordinates of pre- and post-orthodontic landmarks and then applied to each tooth. We evaluate our CLIK-Diffusion on various malocclu- sion cases collected in real-world clinics, and demonstrate its exceptional performance and strong applicability in orthodontic treatment, compared with other state-of-the-art methods. ... See More
Fully Automated Evaluation of Condylar Remodeling After Orthognathic Surgery in Skeletal Class II Patients Using Deep Learning and Landmarks
Fully Automated Evaluation of Condylar Remodeling After Orthognathic Surgery in Skeletal Class II Patients Using Deep Learning and Landmarks
Journal of Dentistry (JoD), 2025
Wei Jia, Han Wu, Lanzhuju Mei, Jiamin Wu, Minjiao Wang+, Zhiming Cui+
PDF Code
Abstract: Condylar remodeling is a key prognostic indicator in maxillofacial surgery for skeletal class II patients. In this study, we present a pioneering approach for fully automated evaluation of postsurgical condylar remodeling in skeletal class II patients. By integrating landmark-guided segmentation and employing the ramus as a stable reference for robust registration, the proposed method demonstrates superior accuracy while improving efficiency by approximately 150 times compared to the manual method. Beyond providing qualitative and quantitative assessments, the resulting patient-specific 3D condylar models enable longitudinal monitoring of postoperative changes, support the early detection of temporomandibular joint (TMJ) dysfunction, and facilitate data-driven personalized management strategies. ... See More
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
MICCAI 2025 [Early Accepted (Top 9%)]
Tao Luo*, Han Wu*, Tong Yang, Dinggang Shen, Zhiming Cui+
PDF Code
Abstract: Accurate dental caries detection from panoramic X-rays plays a pivotal role in preventing lesion progression. However, current detection methods often yield suboptimal accuracy due to subtle contrast variations and diverse lesion morphology of dental caries. In this work, inspired by the clinical workflow where dentists systematically combine whole-image screening with detailed tooth-level inspection, we present DVCTNet, a novel Dual-View Co-Training network for accurate dental caries detection. Our DVCTNet starts with employing automated tooth detection to establish complementary global and local views, which are panoramic X-ray images and the cropped tooth images, and pretrained two vision foundation models on the two views respectively. The global-view foundation model serves as the detection backbone, generating region proposals and global features, while the local-view model extracts detailed features from corresponding cropped tooth patches matched by the region proposals. To effectively integrate information from both views, we introduce a Gated Cross-View Attention (GCV-Atten) module that dynamically fuses dual-view features, enhancing the detection pipeline by integrating the fused features back into the detection model for final caries detection. To rigorously evaluate our DVCTNet, we test it on a public dataset and further validate its performance on a newly curated, high-precision dental caries detection dataset, annotated using both intra-oral images and panoramic X-rays for double verification. Experimental results demonstrate DVCTNet's superior performance against existing state-of-the-art (SOTA) methods on both datasets, indicating the clinical applicability of our method. ... See More
Cephalometric Landmark Detection across Ages with Prototypical Network
Cephalometric Landmark Detection across Ages with Prototypical Network
MICCAI 2024
Han Wu*, Chong Wang*, Lanzhuju Mei, Tong Yang, Min Zhu, Dinggang Shen, Zhiming Cui+
PDF Code Cephalometric Landmark Detection across Ages with Prototypical Network repository stats Poster
Abstract: Automated cephalometric landmark detection is crucial in real-world orthodontic diagnosis. Current studies mainly focus on only adult subjects, neglecting the clinically crucial scenario presented by adolescents whose landmarks often exhibit significantly different appearances compared to adults. Hence, an open question arises about how to develop a unified and effective detection algorithm across various age groups, including adolescents and adults. In this paper, we propose CeLDA, the first work for Cephalometric Landmark Detection across Ages. Our method leverages a prototypical network for landmark detection by comparing image features with landmark prototypes. To tackle the appearance discrepancy of landmarks between age groups, we design new strategies for CeLDA to improve prototype alignment and obtain a holistic estimation of landmark prototypes from a large set of training images. Moreover, a novel prototype relation mining paradigm is introduced to exploit the anatomical relations between the landmark prototypes. Extensive experiments validate the superiority of CeLDA in detecting cephalometric landmarks on both adult and adolescent subjects. To our knowledge, this is the first effort toward developing a unified solution and dataset for cephalometric landmark detection across age groups. ... See More
Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction
IEEE Transactions on Medical Imaging (IEEE TMI), 2024
Zhentao Liu, Yu Fang, Changjian Li, Han Wu, Yuan Liu, Dinggang Shen, Zhiming Cui+,
PDF Code Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction repository stats
Abstract: Cone Beam Computed Tomography (CBCT) is the most widely used imaging method in dentistry. As hundreds of X-ray projections are needed to reconstruct a high-quality CBCT image (i.e., the attenuation field) in traditional algorithms, sparse-view CBCT reconstruction has become a main focus to reduce radiation dose. Several attempts have been made to solve it while still suffering from insufficient data or poor generalization ability for novel patients. This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field. The key insight is when building the volumetric feature, we comply with the multi-view CBCT reconstruction nature and emphasize the view consistency property by geometry-aware spatial feature querying and adaptive feature fusing. Moreover, the prior knowledge information learned from data population guarantees our generalization ability when dealing with sparse view input. Comprehensive evaluations have demonstrated the superiority in terms of reconstruction quality, and the downstream application further validates the feasibility of our method in real-world clinics. ... See More

Others

2
CLIP in Medical Imaging: A Comprehensive Survey
CLIP in Medical Imaging: A Comprehensive Survey
Medical Image Analysis (MedIA), 2025
Zihao Zhao*, Yuxiao Liu*, Han Wu*, Mei Wang, Yonghao Li, Sheng Wang, Lin Teng, Disheng Liu, Zhiming Cui+, Qian Wang+, Dinggang Shen+
PDF Code CLIP in Medical Imaging: A Comprehensive Survey repository stats
Abstract: Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, (1) we first start with a brief introduction to the fundamentals of CLIP methodology; (2) then we investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) furthermore, we explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; (4) finally, we discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this comprehensive survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. ... See More
Multi-View Vertebra Localization and Identification from CT Images
Multi-View Vertebra Localization and Identification from CT Images
MICCAI 2023
Han Wu, Jiadong Zhang, Yu Fang, Zhentao Liu, Nizhuan Wang, Zhiming Cui+, Dinggang Shen+
PDF Code Multi-View Vertebra Localization and Identification from CT Images repository stats Supp Slides Poster
Abstract: Accurately localizing and identifying vertebra from CT images is crucial for various clinical applications. However, most existing efforts are performed on 3D with cropping patch operation, suffering from the large computation costs and limited global information. In this paper, we propose a multi-view vertebra localization and identification from CT images, converting the 3D problem into a 2D localization and identification task on different views. Without the limitation of the 3D cropped patch, our method can learn the multi-view global information naturally. Moreover, to better capture the anatomical structure information from different view perspectives, a multi-view contrastive learning strategy is developed to pre-train the backbone. Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae. Evaluation results demonstrate that, with only two 2D networks, our method can localize and identify vertebrae in CT images accurately, and outperforms the state-of-the-art methods consistently ... See More

Preprint

Two-Stage Robust 3D CTA–2D DSA Alignment via Vascular-Aware Rigid and Pyramid-Based Hierarchical Non-Rigid Registration
Xiaosong Xiong, Caiwen Jiang, Han Wu, Xiao Zhang, Peng Wu, Yanli Song, Xinyi Zhang, Dijia Wu, Dinggang Shen+
Medical Image Analysis (MedIA) · (Major Revision)
COMET: Controllable Orthodontic Video Editing with Multi-View Estimation
Lanzhuju Mei, Yulong Dou, Han Wu, Guo Chen, Zhiming Cui, Dinggang Shen+
IEEE Transaction on Image Processing (IEEE TIP) · (Under Review)

Awards

Services

Conference Reviewers

Journal Reviewers

Membership

Teaching Assistant

  • BME2113 Algorithms Design And Analysis (Python) @ ShanghaiTech, 2023 Fall

Other Services

  • BaiDu PaddlePaddle Developer Expert (PPDE)

Invited Talks

AI in Digital Dentistry

Guest Talk, University of Edinburgh, 2026

Knowledge-driven Landmark Detection in Medical Images

Stars Talk, BME School, ShanghaiTech University, 2025

Miscellaneous

I'm a big fan of Taylor Swift, Jay Chou, and JJ Lin.
I love playing guitar whenever I feel stressed. Masaaki Kishibe is my favourite guitarist.
I enjoy playing LOL with my friends. I was also a Master player in LOLM.

Contact

Email

wuhan2022 [at] shanghaitech.edu.cn

Address

393 Middle Huaxia Road, Pudong, Shanghai, China

Office

IDEA Lab @ BME Building, ShanghaiTech University, Shanghai, China