Search Results for author: Jiahao Lu

Found 13 papers, 9 papers with code

3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain

no code implementations1 May 2024 Yuran Zhu, Guanhua Wang, Yuning Gu, Walter Zhao, Jiahao Lu, Junqing Zhu, Christina J. MacAskill, Andrew Dupuis, Mark A. Griswold, Dan Ma, Chris A. Flask, Xin Yu

We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.

Unsegment Anything by Simulating Deformation

1 code implementation3 Apr 2024 Jiahao Lu, Xingyi Yang, Xinchao Wang

Foundation segmentation models, while powerful, pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click, potentially leading to copyright infringement or malicious misuse.


Unsupervised Template-assisted Point Cloud Shape Correspondence Network

no code implementations25 Mar 2024 Jiacheng Deng, Jiahao Lu, Tianzhu Zhang

Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds.

BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation

1 code implementation22 Mar 2024 Jiahao Lu, Jiacheng Deng, Tianzhu Zhang

To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer.

3D Instance Segmentation Decoder +1

SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence

1 code implementation CVPR 2023 Jiacheng Deng, Chuxin Wang, Jiahao Lu, Jianfeng He, Tianzhu Zhang, Jiyang Yu, Zhe Zhang

The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.

Ranked #2 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)

3D Dense Shape Correspondence

Sliding at first order: Higher-order momentum distributions for discontinuous image registration

no code implementations14 Mar 2023 Lili Bao, Jiahao Lu, Shihui Ying, Stefan Sommer

In this paper, we propose a new approach to deformable image registration that captures sliding motions.

Image Registration

Query Refinement Transformer for 3D Instance Segmentation

no code implementations ICCV 2023 Jiahao Lu, Jiacheng Deng, Chuxin Wang, Jianfeng He, Tianzhu Zhang

Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.

3D Instance Segmentation Decoder +2

cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis

2 code implementations28 Oct 2022 Jiahao Lu, CHONG YIN, Kenny Erleben, Michael Bachmann Nielsen, Sune Darkner

Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis.

Active Learning Attribute +1

Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis

2 code implementations27 Jun 2022 Jiahao Lu, CHONG YIN, Oswin Krause, Kenny Erleben, Michael Bachmann Nielsen, Sune Darkner

Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge.

Clinical Knowledge Contrastive Learning

APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers

1 code implementation CVPR 2022 Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao, Xiangyu He, Jian Cheng

Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.

Federated Learning

Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study

2 code implementations30 Mar 2021 Jiahao Lu, Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje

We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.

Generative Adversarial Network Image Registration +3

CoMIR: Contrastive Multimodal Image Representation for Registration

1 code implementation NeurIPS 2020 Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje

We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).

Image-to-Image Translation

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

2 code implementations23 Oct 2019 Jiahao Lu, Nataša Sladoje, Christina Runow Stark, Eva Darai Ramqvist, Jan-Michaél Hirsch, Joakim Lindblad

The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.

Classification General Classification +3

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