Search Results for author: Shuo Li

Found 58 papers, 15 papers with code

EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis

no code implementations ECCV 2020 Rongchang Zhao, Xuanlin Chen, Zailiang Chen, Shuo Li

Today's computer-aided diagnosis (CAD) model is still far from the clinical practice of glaucoma detection, mainly due to the training bias originating from 1) the normal-abnormal class imbalance and 2) the rare but significant hard samples in fundus images.


Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking

1 code implementation23 Aug 2023 Zhiheng Li, Yu Lin, Yubo Cui, Shuo Li, Zheng Fang

3D single object tracking with LiDAR points is an important task in the computer vision field.

3D Single Object Tracking Object Tracking

Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training

no code implementations14 Jul 2023 Xiaofei Chen, Yuting He, Cheng Xue, Rongjun Ge, Shuo Li, Guanyu Yang

To address these issues, we propose the Knowledge-Boosting Contrastive Vision-Language Pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision-language semantic consistency.

Clinical Knowledge Representation Learning +1

TRAC: Trustworthy Retrieval Augmented Chatbot

no code implementations7 Jul 2023 Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani

Although conversational AIs have demonstrated fantastic performance, they often generate incorrect information, or hallucinations.

Bayesian Optimization Chatbot +4

Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations

1 code implementation13 Jun 2023 Shuo Li, Mehrdad Yaghoobi

Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information about the material composition of the imaged scene.

Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior

no code implementations12 Jun 2023 Shuo Li, Mehrdad Yaghoobi

It is shown that LRS-PnP is able to predict missing pixels and bands even when all spectral bands of the image are missing.

Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training

1 code implementation CVPR 2023 Yuting He, Guanyu Yang, Rongjun Ge, Yang Chen, Jean-Louis Coatrieux, Boyu Wang, Shuo Li

We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions.

Geometric Matching Representation Learning

CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision

no code implementations ICCV 2023 Shuo Li, Yue He, Weiming Zhang , Wei zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang

Current state-of-the-art semi-supervised semantic segmentation (SSSS) methods typically adopt pseudo labeling and consistency regularization between multiple learners with different perturbations.

Semi-Supervised Semantic Segmentation

Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

no code implementations13 Dec 2022 Zhenyu Wu, Lin Wang, Wei Wang, Qing Xia, Chenglizhao Chen, Aimin Hao, Shuo Li

This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset.

Active Learning Adversarial Attack +3

Synthetic Data Supervised Salient Object Detection

1 code implementation25 Oct 2022 Zhenyu Wu, Lin Wang, Wei Wang, Tengfei Shi, Chenglizhao Chen, Aimin Hao, Shuo Li

In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model.

Code Generation object-detection +2

Contrastive Re-localization and History Distillation in Federated CMR Segmentation

no code implementations MICCAI 2022 2022 Xiaoming Qi, Guanyu Yang, Yuting He, Wangyan Liu, Ali Islam, Shuo Li

In this work, a cross-center cross-sequence medical image segmentation FL framework (FedCRLD) is proposed for the first time to facilitate multi-center multi-sequence CMR segmentation.

Federated Learning Image Segmentation +2

Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model

no code implementations16 Jul 2022 Xiaolin Chen, Xuemeng Song, Liqiang Jing, Shuo Li, Linmei Hu, Liqiang Nie

To address these limitations, we propose a novel dual knowledge-enhanced generative pretrained language model for multimodal task-oriented dialog systems (DKMD), consisting of three key components: dual knowledge selection, dual knowledge-enhanced context learning, and knowledge-enhanced response generation.

Language Modelling Response Generation

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

1 code implementation4 Jul 2022 Kai-Ni Wang, Yuting He, Shuaishuai Zhuang, Juzheng Miao, Xiaopu He, Ping Zhou, Guanyu Yang, Guang-Quan Zhou, Shuo Li

Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans.

Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation

1 code implementation21 Jun 2022 Dong Liang, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang, Shuo Li

The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.


XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention

1 code implementation15 Jun 2022 Jiacheng Shi, Yuting He, Youyong Kong, Jean-Louis Coatrieux, Huazhong Shu, Guanyu Yang, Shuo Li

An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration.

Deformable Medical Image Registration Image Registration +2

PAC-Wrap: Semi-Supervised PAC Anomaly Detection

no code implementations22 May 2022 Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving.

Autonomous Driving Unsupervised Anomaly Detection

MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation

2 code implementations10 May 2022 Zhangfu Dong, Yuting He, Xiaoming Qi, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Guanyu Yang, Shuo Li

The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).

Image Segmentation Medical Image Segmentation +1

Towards PAC Multi-Object Detection and Tracking

no code implementations15 Apr 2022 Shuo Li, Sangdon Park, Xiayan Ji, Insup Lee, Osbert Bastani

Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation.

Autonomous Navigation Conformal Prediction +2

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI

no code implementations7 Jan 2022 Jianfeng Zhao, Dengwang Li, Shuo Li

In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI.

feature selection Tumor Segmentation

Dialogue Inspectional Summarization with Factual Inconsistency Awareness

no code implementations5 Nov 2021 Leilei Gan, Yating Zhang, Kun Kuang, Lin Yuan, Shuo Li, Changlong Sun, Xiaozhong Liu, Fei Wu

Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.

dialogue summary Medical Diagnosis

Optimal Resource Allocation for Serverless Queries

no code implementations19 Jul 2021 Anish Pimpley, Shuo Li, Anubha Srivastava, Vishal Rohra, Yi Zhu, Soundararajan Srinivasan, Alekh Jindal, Hiren Patel, Shi Qiao, Rathijit Sen

We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries.

Data Augmentation

Information Avoidance and Overvaluation in Sequential Decision Making under Epistemic Constraints

no code implementations9 Jun 2021 Shuo Li, Matteo Pozzi

We focus on the value of collecting information at current time, and on that of collecting sequential information, we illustrate how these values are related and we discuss how IA and IOV can occur in those settings.

Decision Making Management

EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation

no code implementations8 Jun 2021 Yuting He, Rongjun Ge, Xiaoming Qi, Guanyu Yang, Yang Chen, Youyong Kong, Huazhong Shu, Jean-Louis Coatrieux, Shuo Li

3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.

Ensemble Learning

Doping isolated one-dimensional antiferro-magnetic semiconductor Vanadium tetrasulfide ($VS_4$) nanowires with carriers induces half-metallicity

no code implementations29 Jan 2021 Shuo Li, Junjie He, Petr Nachtigall, Lukas Grajciar, Federico Brivio

Our results indicate that both bulk phase and isolated $VS_4$ NWs are semiconductors with band gaps of 2. 24 and 2. 64 eV, respectively, and that they prefer the antiferromagnetic (AFM) ground state based on DFT calculations.

Materials Science Strongly Correlated Electrons Chemical Physics Computational Physics Quantum Physics A.0

Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression

no code implementations23 Dec 2020 Haoliang Sun, XianTong Zhen, Chris Bailey, Parham Rasoulinejad, Yilong Yin, Shuo Li

The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment.


X-ray flares from the stellar tidal disruption by a candidate supermassive black hole binary

no code implementations22 Dec 2020 Xinwen Shu, Wenjie Zhang, Shuo Li, Ning Jiang, Liming Dou, Zhen Yan, Fu-Guo Xie, Rongfeng Shen, Luming Sun, Fukun Liu, Tinggui Wang

Optical transient surveys have led to the discovery of dozens of stellar tidal disruption events (TDEs) by massive black hole in the centers of galaxies.

High Energy Astrophysical Phenomena Astrophysics of Galaxies

Digital Quantum Simulation of Floquet Topological Phases with a Solid-State Quantum Simulator

no code implementations10 Dec 2020 Bing Chen, Shuo Li, Xianfei Hou, Feifei Zhou, Peng Qian, Feng Mei, Suotang Jia, Nanyang Xu, Heng Shen

Quantum simulator with the ability to harness the dynamics of complex quantum systems has emerged as a promising platform for probing exotic topological phases.

Quantum Physics

PAC Confidence Predictions for Deep Neural Network Classifiers

no code implementations ICLR 2021 Sangdon Park, Shuo Li, Insup Lee, Osbert Bastani

In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs.

Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images

no code implementations ECCV 2020 Yuting He, Tiantian Li, Guanyu Yang, Youyong Kong, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Jean-Louis Dillenseger, Shuo Li

Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation.

Image Registration Medical Image Registration

Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation

no code implementations28 Apr 2020 Zhongyi Han, Benzheng Wei, Yilong Yin, Shuo Li

In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the spinal medical report generation.

Decision Making Logical Reasoning +2

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

no code implementations2 Feb 2020 Guang Yang, Jun Chen, Zhifan Gao, Shuo Li, Hao Ni, Elsa Angelini, Tom Wong, Raad Mohiaddin, Eva Nyktari, Ricardo Wage, Lei Xu, Yanping Zhang, Xiuquan Du, Heye Zhang, David Firmin, Jennifer Keegan

Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).


Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

no code implementations24 Oct 2019 Shuo Li, Osbert Bastani

We build on the idea of model predictive shielding (MPS), where a backup controller is used to override the learned policy as needed to ensure safety.

Learning Theory reinforcement-learning +2

Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

no code implementations20 Jul 2019 Dong Zhang, Guang Yang, Shu Zhao, Yanping Zhang, Heye Zhang, Shuo Li

The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.

Multiview Learning regression

Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network

1 code implementation14 Jun 2018 Shumao Pang, Stephanie Leung, Ilanit Ben Nachum, Qianjin Feng, Shuo Li

The CARN architecture is composed of a cascade amplifier network (CAN) for expressive feature embedding and a linear regression model for multiple indices estimation.


Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling

no code implementations14 Jun 2018 Wufeng Xue, Gary Brahm, Stephanie Leung, Ogla Shmuilovich, Shuo Li

Experiments on 1440 myocardium segments of 90 subjects from short axis MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable performance, with correlation of 0. 926 for motion score index estimation and accuracy of 77. 4\% for motion scoring.

Anomaly Detection

VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination

no code implementations10 Jun 2018 Suyu Dong, Gongning Luo, Kuanquan Wang, Shaodong Cao, Ashley Mercado, Olga Shmuilovich, Henggui Zhang, Shuo Li

And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.

Left Ventricle Segmentation Self-Learning

Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm

no code implementations10 Jun 2017 Chenchu Xu, Lei Xu, Zhifan Gao, Shen zhao, Heye Zhang, Yanping Zhang, Xiuquan Du, Shu Zhao, Dhanjoo Ghista, Shuo Li

Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.

Management Time Series Analysis

Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences

no code implementations6 Jun 2017 Liansheng Wang, Shusheng Li, Shuo Li

With the uniform distribution of random points, our proposed method achieves more accurate results compared with other methods, which demonstrates the robustness and accuracy for the volume calculation of CT lung lesions.

Computed Tomography (CT)

Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness

no code implementations6 Jun 2017 Wufeng Xue, Andrea Lum, Ashley Mercado, Mark Landis, James Warringto, Shuo Li

Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac diseases, yet still a challenge due to the high variability of cardiac structure and the complexity of temporal dynamics.

feature selection

Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network

no code implementations26 May 2017 Wufeng Xue, Ilanit Ben Nachum, Sachin Pandey, James Warrington, Stephanie Leung, Shuo Li

Accurate estimation of regional wall thicknesses (RWT) of left ventricular (LV) myocardium from cardiac MR sequences is of significant importance for identification and diagnosis of cardiac disease.

Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning

2 code implementations25 May 2017 Wufeng Xue, Ali Islam, Mousumi Bhaduri, Shuo Li

However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences.

Feature Engineering regression

Towards Direct Medical Image Analysis without Segmentation

no code implementations21 Oct 2015 Xiantong Zhen, Shuo Li

Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise.

Supervised Descriptor Learning for Multi-Output Regression

no code implementations CVPR 2015 Xiantong Zhen, Zhijie Wang, Mengyang Yu, Shuo Li

In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression.

Head Pose Estimation regression

Purine: A bi-graph based deep learning framework

1 code implementation19 Dec 2014 Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan

In this paper, we introduce a novel deep learning framework, termed Purine.

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