Han Wu
🥕 carrot

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/

Han Wu 吴瀚

I am a Ph.D. student at the School of Biomedical Engineering, ShanghaiTech University, supervised by Prof. Dinggang Shen and Prof. Zhiming Cui. I am also fortunate to collaborate closely with Dr. Chong Wang (Ph.D.) and Dr. Jiawei Huang (M.D.). Prior to this, I spent four years at Wuhan University of Technology (WUT), where I had led a team won several national and regional competitions. I graduated with a B.Eng. degree in 2022 with the hornor of outstanding graduate.

My current research is centered on medical image analysis with a special focus on cardiography and digital dentistry. I am particularly interested in the following two specific areas:

Landmark Detection and Matching: Cephalometric landmark detection across various age groups and modalities, as well as landmark tracking and matching in ultrasound/DSA video.
Label-Efficient Learning in Medical Images: Self-/semi-/weak-supervised learning for disease detection and medical image segmentation.

Education

ShanghaiTech University, Shanghai, China (Sept. 2022 - Present)
Ph.D. in Computer Science
Supervisors: Prof. Dinggang Shen, Prof. Zhiming Cui
ShanghaiTech University
Wuhan University of Technology, Wuhan, China (Sept. 2018 - June 2022)
B.E. in Information Engineering
Rank: 8/160
Wuhan University of Technology

News

Publications

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] | [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
CLIP in Medical Imaging: A Comprehensive Survey
arXiv [under review]
Zihao Zhao*, Yuxiao Liu*, Han Wu*, Yonghao Li, Sheng Wang, Lin Teng, Disheng Liu, Zhiming Cui+, Qian Wang+, Dinggang Shen+
[PDF] | [CODE]
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
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]
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
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] | [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

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Teaching Assistant

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

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  • BaiDu PaddlePaddle Developer Expert (PPDE)

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