Search Results for author: Ke Yan

Found 80 papers, 29 papers with code

Exploring the landscape of large language models: Foundations, techniques, and challenges

no code implementations18 Apr 2024 Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari

In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes.

Anchor-based Robust Finetuning of Vision-Language Models

no code implementations9 Apr 2024 Jinwei Han, Zhiwen Lin, Zhongyisun Sun, Yingguo Gao, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia

Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics.

Language Modelling Zero-Shot Learning

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

no code implementations7 Apr 2024 Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan, Tony C. W. Mok, Ke Yan, Jiawen Yao, Xin Chen, Zaiyi Liu, Le Lu, Ling Zhang, Minfeng Xu

In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution.


LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

no code implementations26 Mar 2024 Yunpeng Luo, Junlong Du, Ke Yan, Shouhong Ding

In response to this, we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images.

Image Generation

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models

no code implementations19 Feb 2024 Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Kun Kuang, Chao Wu

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.

Image Captioning Question Answering +1

VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding

no code implementations14 Dec 2023 Yi Xin, Junlong Du, Qiang Wang, Zhiwen Lin, Ke Yan

Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3. 96%(1. 34%) relative improvement compared to single-task full fine-tuning, while utilizing merely ~1% (0. 36%) trainable parameters of the pre-trained model.

Scene Understanding Transfer Learning

MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning

no code implementations14 Dec 2023 Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding

On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group.

Language Modelling Multi-Task Learning +1

UAE: Universal Anatomical Embedding on Multi-modality Medical Images

1 code implementation25 Nov 2023 Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, JingJing Lu, Xianghua Ye, Ke Yan, Yong Xia

They use self-supervised learning to acquire a discriminative embedding for each voxel within the image.

Self-Supervised Learning

SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation

1 code implementation25 Nov 2023 Lin Tian, Zi Li, Fengze Liu, Xiaoyu Bai, Jia Ge, Le Lu, Marc Niethammer, Xianghua Ye, Ke Yan, Daikai Jin

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level.

Image Registration Medical Image Registration

Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning

no code implementations15 Nov 2023 Xiaoshuang Chen, Zhongyi Sun, Ke Yan, Shouhong Ding, Hongtao Lu

In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD).

Class Incremental Learning Incremental Learning

HODN: Disentangling Human-Object Feature for HOI Detection

no code implementations20 Aug 2023 Shuman Fang, Zhiwen Lin, Ke Yan, Jie Li, Xianming Lin, Rongrong Ji

However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection.

Human Detection Human-Object Interaction Detection +3

SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation

no code implementations9 Aug 2023 Fan Bai, Ke Yan, Xiaoyu Bai, Xinyu Mao, Xiaoli Yin, Jingren Zhou, Yu Shi, Le Lu, Max Q. -H. Meng

We evaluate our method on liver tumor segmentation and achieve state-of-the-art performance, outperforming traditional fine-tuning with only 6% of tunable parameters, also achieving 94% of full-data performance by labeling only 5% of the data.

Lesion Segmentation Tumor Segmentation +1

Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification

no code implementations28 Jul 2023 Ke Yan, Dakai Jin, Dazhou Guo, Minfeng Xu, Na Shen, Xian-Sheng Hua, Xianghua Ye, Le Lu

Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information.

Anatomy Multi-Task Learning

SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid

1 code implementation19 Jul 2023 Zi Li, Lin Tian, Tony C. W. Mok, Xiaoyu Bai, Puyang Wang, Jia Ge, Jingren Zhou, Le Lu, Xianghua Ye, Ke Yan, Dakai Jin

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens.

Image Registration

Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images

no code implementations7 Jul 2023 Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, Tony C. W. Mok, Zi Li, Minfeng Xu, Jingren Zhou, Le Lu, Dakai Jin, Xianghua Ye, JingJing Lu, Ke Yan

We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images.

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

no code implementations28 Jun 2023 Fakai Wang, Chi-Tung Cheng, Chien-Wei Peng, Ke Yan, Min Wu, Le Lu, Chien-Hung Liao, Ling Zhang

In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1, 631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks.

Lesion Detection Specificity

Model-agnostic explainable artificial intelligence for object detection in image data

no code implementations30 Mar 2023 Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari

The experimentations on various object detection datasets and models showed that BODEM can be effectively used to explain the behavior of object detectors and reveal their vulnerabilities.

Data Augmentation Explainable artificial intelligence +3

CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising

1 code implementation journal 2023 Kang Xu, Weixin Li, Xia Wang, Xiaoyan Hu, Ke Yan, Xiaojie Wang, Xuan Dong

Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other.

Image Denoising Jpeg Compression Artifact Reduction +1

Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis

1 code implementation ICCV 2023 Yankai Jiang, Mingze Sun, Heng Guo, Xiaoyu Bai, Ke Yan, Le Lu, Minfeng Xu

Alice introduces a new contrastive learning strategy which encourages the similarity between views that are diversely mined but with consistent high-level semantics, in order to learn invariant anatomical features.

Contrastive Learning Image Segmentation +4

Few-Shot Object Detection via Variational Feature Aggregation

1 code implementation31 Jan 2023 Jiaming Han, Yuqiang Ren, Jian Ding, Ke Yan, Gui-Song Xia

As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples.

Few-Shot Object Detection Meta-Learning +3

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding

1 code implementation5 Dec 2022 Heng Guo, Jianfeng Zhang, Ke Yan, Le Lu, Minfeng Xu

For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs.

Anatomy Computed Tomography (CT) +5

A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation

no code implementations2 Aug 2022 Minfeng Xu, Heng Guo, Jianfeng Zhang, Ke Yan, Le Lu

Accurate and robust abdominal multi-organ segmentation from CT imaging of different modalities is a challenging task due to complex inter- and intra-organ shape and appearance variations among abdominal organs.

Organ Segmentation Segmentation

Inferring from References with Differences for Semi-Supervised Node Classification on Graphs

1 code implementation Mathematics 2022 Yi Luo, Guangchun Luo, Ke Yan, Aiguo Chen

Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years.

Node Classification

Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning

no code implementations12 Oct 2021 Bowen Li, Dar-In Tai, Ke Yan, Yi-Cheng Chen, Shiu-Feng Huang, Tse-Hwa Hsu, Wan-Ting Yu, Jing Xiao, Le Lu, Adam P. Harrison

High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0. 85, 0. 90, and 0. 93, respectively.

Transformer-based Dual Relation Graph for Multi-label Image Recognition

1 code implementation ICCV 2021 Jiawei Zhao, Ke Yan, Yifan Zhao, Xiaowei Guo, Feiyue Huang, Jia Li

Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.

Multi-Label Classification Relation

Heterogeneous Relational Complement for Vehicle Re-identification

1 code implementation ICCV 2021 Jiajian Zhao, Yifan Zhao, Jia Li, Ke Yan, Yonghong Tian

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations.

Vehicle Re-Identification

Discriminator-Free Generative Adversarial Attack

1 code implementation20 Jul 2021 ShaoHao Lu, Yuqiao Xian, Ke Yan, Yi Hu, Xing Sun, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng

The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images.

Adversarial Attack Disentanglement

Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

2 code implementations16 Jun 2021 Yi Luo, Aiguo Chen, Ke Yan, Ling Tian

Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.

Node Classification Node Property Prediction

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

no code implementations5 May 2021 YouBao Tang, Ke Yan, Jinzheng Cai, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu

PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.

Lesion Segmentation Segmentation

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

no code implementations3 May 2021 YouBao Tang, Jinzheng Cai, Ke Yan, Lingyun Huang, Guotong Xie, Jing Xiao, JingJing Lu, Gigin Lin, Le Lu

Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as universal lesion segmentation (ULS).

Computed Tomography (CT) Lesion Segmentation +2

Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings

no code implementations12 Apr 2021 Bowen Li, Xinping Ren, Ke Yan, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Dar-In Tai, Adam P. Harrison

Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives.

Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining

no code implementations9 Mar 2021 Jieneng Chen, Ke Yan, Yu-Dong Zhang, YouBao Tang, Xun Xu, Shuwen Sun, Qiuping Liu, Lingyun Huang, Jing Xiao, Alan L. Yuille, Ya zhang, Le Lu

(2) The sampled deep vertex features with positional embedding are mapped into a sequential space and decoded by a multilayer perceptron (MLP) for semantic classification.


Memory-Associated Differential Learning

2 code implementations10 Feb 2021 Yi Luo, Aiguo Chen, Bei Hui, Ke Yan

Conventional Supervised Learning approaches focus on the mapping from input features to output labels.

Link Prediction

Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

1 code implementation CVPR 2021 Jinzheng Cai, YouBao Tang, Ke Yan, Adam P. Harrison, Jing Xiao, Gigin Lin, Le Lu

In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.

3D Object Tracking

Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression

no code implementations30 Aug 2020 Jinzheng Cai, Ke Yan, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu, Adam P. Harrison

Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians.

Lesion Detection regression

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

no code implementations27 Aug 2020 Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.

CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization

no code implementations ECCV 2020 Yuxi Li, Weiyao Lin, John See, Ning Xu, Shugong Xu, Ke Yan, Cong Yang

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.

Action Detection Spatio-Temporal Action Localization +1

One Click Lesion RECIST Measurement and Segmentation on CT Scans

no code implementations21 Jul 2020 Youbao Tang, Ke Yan, Jing Xiao, Ranold M. Summers

Based on the results of the first network, the second one refines the lesion segmentation and RECIST estimation.

Lesion Segmentation Segmentation

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets

no code implementations28 May 2020 Ke Yan, Jinzheng Cai, Adam P. Harrison, Dakai Jin, Jing Xiao, Le Lu

First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.

Ranked #5 on Medical Object Detection on DeepLesion (using extra training data)

Lesion Detection Medical Object Detection +1

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

1 code implementation21 Jan 2020 Jinzheng Cai, Adam P. Harrison, Youjing Zheng, Ke Yan, Yuankai Huo, Jing Xiao, Lin Yang, Le Lu

This is the goal of our work, where we develop a powerful system to harvest missing lesions from the DeepLesion dataset at high precision.

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

14 code implementations12 Aug 2019 Ke Yan, You-Bao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

Computed Tomography (CT) Lesion Detection +2

A self-attention based deep learning method for lesion attribute detection from CT reports

no code implementations30 Apr 2019 Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu

In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.

Attribute Sentence

Fine-grained lesion annotation in CT images with knowledge mined from radiology reports

no code implementations4 Mar 2019 Ke Yan, Yifan Peng, Zhiyong Lu, Ronald M. Summers

To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.


ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining

1 code implementation18 Jan 2019 Youbao Tang, Ke Yan, Yu-Xing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers

To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.

Computed Tomography (CT) Lesion Detection

Attention Driven Person Re-identification

no code implementations13 Oct 2018 Fan Yang, Ke Yan, Shijian Lu, Huizhu Jia, Xiaodong Xie, Wen Gao

Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.

Person Re-Identification

Deep Learning for Image Denoising: A Survey

no code implementations11 Oct 2018 Chunwei Tian, Yong Xu, Lunke Fei, Ke Yan

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing.

BIG-bench Machine Learning Image Denoising

CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement

no code implementations18 Jul 2018 Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.

Computed Tomography (CT) Image Enhancement +3

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

no code implementations25 Jan 2018 Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers

Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.

Generative Adversarial Network Lesion Segmentation +2

Information Design in Crowdfunding under Thresholding Policies

no code implementations12 Sep 2017 Wen Shen, Jacob W. Crandall, Ke Yan, Cristina V. Lopes

We introduce a heuristic algorithm to dynamically compute information-disclosure policies for the entrepreneur, followed by an empirical evaluation to demonstrate its competitiveness over the widely-adopted immediate-disclosure policy.

Learning a Repression Network for Precise Vehicle Search

1 code implementation8 Aug 2017 Qiantong Xu, Ke Yan, Yonghong Tian

The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from large-scale image databases.

Attribute Multi-Task Learning +1

Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

2 code implementations12 Jul 2017 Ke Yan, Le Lu, Ronald M. Summers

In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem.

Anomaly Detection regression

Local Multiple Directional Pattern of Palmprint Image

no code implementations21 Jul 2016 Lunke Fei, Jie Wen, Zheng Zhang, Ke Yan, Zuofeng Zhong

Conventional methods usually capture the only one of the most dominant direction of palmprint images.

Learning Domain-Invariant Subspace using Domain Features and Independence Maximization

2 code implementations15 Mar 2016 Ke Yan, Lu Kou, David Zhang

In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space.

Domain Adaptation

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