no code implementations • 6 May 2024 • Jinwei Han, Yingguo Gao, Zhiwen Lin, Ke Yan, Shouhong Ding, Yuan Gao, Gui-Song Xia
Specifically, we introduce a Dual Attention Block (DAB) for visual-semantic relationship mining, which enriches visual information by multi-level feature fusion and conducts spatial attention for visual to semantic embedding.
no code implementations • 1 May 2024 • Milad Moradi, Ke Yan, David Colwell, Rhona Asgari
Leveraging a machine learning model, our method accurately identifies UI controls from software screenshots and constructs a graph representing contextual and spatial relationships between the controls.
no code implementations • 23 Apr 2024 • Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan, Le Lu, Jiebo Luo, Ling Zhang
In this paper, we extend the scope of Med-VLP to encompass 3D images, specifically targeting full-body scenarios, by using a multimodal dataset of CT images and reports.
no code implementations • 18 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.
no code implementations • 9 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.
no code implementations • 7 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.
no code implementations • 4 Apr 2024 • Qinji Yu, Yirui Wang, Ke Yan, Haoshen Li, Dazhou Guo, Li Zhang, Le Lu, Na Shen, Qifeng Wang, Xiaowei Ding, Xianghua Ye, Dakai Jin
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology.
no code implementations • 26 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.
no code implementations • 22 Mar 2024 • Heng Guo, Jianfeng Zhang, Jiaxing Huang, Tony C. W. Mok, Dazhou Guo, Ke Yan, Le Lu, Dakai Jin, Minfeng Xu
In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D.
no code implementations • 29 Feb 2024 • Tony C. W. Mok, Zi Li, Yunhao Bai, Jianpeng Zhang, Wei Liu, Yan-Jie Zhou, Ke Yan, Dakai Jin, Yu Shi, Xiaoli Yin, Le Lu, Ling Zhang
Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations.
no code implementations • 19 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.
no code implementations • 14 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.
no code implementations • 14 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.
1 code implementation • 25 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.
1 code implementation • 25 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.
Ranked #8 on Image Registration on Unpaired-abdomen-CT
no code implementations • 15 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).
no code implementations • 20 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.
no code implementations • 9 Aug 2023 • Qiang Wang, Junlong Du, Ke Yan, Shouhong Ding
We propose that the key lies in explicitly modeling the motion cues flowing in video frames.
no code implementations • 9 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.
no code implementations • 28 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.
1 code implementation • 19 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.
1 code implementation • 17 Jul 2023 • Ke Yan, Xiaoli Yin, Yingda Xia, Fakai Wang, Shu Wang, Yuan Gao, Jiawen Yao, Chunli Li, Xiaoyu Bai, Jingren Zhou, Ling Zhang, Le Lu, Yu Shi
Liver tumor segmentation and classification are important tasks in computer aided diagnosis.
no code implementations • 7 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.
no code implementations • 28 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.
no code implementations • CVPR 2023 • Mingze Yuan, Yingda Xia, Hexin Dong, ZiFan Chen, Jiawen Yao, Mingyan Qiu, Ke Yan, Xiaoli Yin, Yu Shi, Xin Chen, Zaiyi Liu, Bin Dong, Jingren Zhou, Le Lu, Ling Zhang, Li Zhang
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant.
1 code implementation • 30 Mar 2023 • Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari
In this paper, we design and implement a black-box explanation method named Black-box Object Detection Explanation by Masking (BODEM) through adopting a hierarchical random masking approach for AI-based object detection systems.
1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2023 • Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, Yong Xu
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery.
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.
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.
no code implementations • 1 Feb 2023 • Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Jingren Zhou, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, Dakai Jin
Deep learning empowers the mainstream medical image segmentation methods.
1 code implementation • 31 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.
no code implementations • ICCV 2023 • Jieneng Chen, Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
no code implementations • ICCV 2023 • Zhanghexuan Ji, Dazhou Guo, Puyang Wang, Ke Yan, Le Lu, Minfeng Xu, Qifeng Wang, Jia Ge, Mingchen Gao, Xianghua Ye, Dakai Jin
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
1 code implementation • 5 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.
no code implementations • 2 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.
no code implementations • CVPR 2022 • Xixia Xu, Yingguo Gao, Ke Yan, Xue Lin, Qi Zou
We reformulate the regression-based HPE from the perspective of classification.
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.
Ranked #1 on Node Classification on Amazon Photo
1 code implementation • CVPR 2022 • Hanjun Li, Xingjia Pan, Ke Yan, Fan Tang, Wei-Shi Zheng
Object detection under imperfect data receives great attention recently.
1 code implementation • CVPR 2022 • Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia
Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability.
no code implementations • 12 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.
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.
Ranked #8 on Multi-Label Classification on PASCAL VOC 2007
no code implementations • 23 Sep 2021 • Fengze Liu, Ke Yan, Adam Harrison, Dazhou Guo, Le Lu, Alan Yuille, Lingyun Huang, Guotong Xie, Jing Xiao, Xianghua Ye, Dakai Jin
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration.
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.
1 code implementation • 20 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.
no code implementations • CVPR 2021 • Yifan Zhao, Ke Yan, Feiyue Huang, Jia Li
Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects.
Ranked #29 on Fine-Grained Image Classification on CUB-200-2011
2 code implementations • 16 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.
Ranked #1 on Node Classification on Cora Full
no code implementations • 5 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.
no code implementations • 3 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).
no code implementations • 12 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.
no code implementations • 9 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.
2 code implementations • 10 Feb 2021 • Yi Luo, Aiguo Chen, Bei Hui, Ke Yan
Conventional Supervised Learning approaches focus on the mapping from input features to output labels.
Ranked #1 on Link Property Prediction on ogbl-ddi
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.
1 code implementation • 4 Dec 2020 • Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Dazhou Guo, Adam P. Harrison, YouBao Tang, Jing Xiao, JingJing Lu, Le Lu
We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM).
1 code implementation • 5 Sep 2020 • Ke Yan, Jinzheng Cai, Youjing Zheng, Adam P. Harrison, Dakai Jin, YouBao Tang, Yuxing Tang, Lingyun Huang, Jing Xiao, Le Lu
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
no code implementations • 30 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.
no code implementations • 29 Aug 2020 • Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung-Ying Ho, Jinzheng Cai, Adam P. Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin
Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.
no code implementations • 27 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.
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.
no code implementations • 7 Aug 2020 • Bowen Li, Ke Yan, Dar-In Tai, Yuankai Huo, Le Lu, Jing Xiao, Adam P. Harrison
Ultrasound (US) is a critical modality for diagnosing liver fibrosis.
no code implementations • 21 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.
no code implementations • 28 Jun 2020 • Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng, Ashwin Raju, Ke Yan, Bennett A. Landman, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison
To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT).
no code implementations • 28 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)
no code implementations • 27 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.
1 code implementation • 21 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.
14 code implementations • 12 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.
Ranked #7 on Medical Object Detection on DeepLesion
no code implementations • 30 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.
3 code implementations • CVPR 2019 • Ke Yan, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers
In radiologists' routine work, one major task is to read a medical image, e. g., a CT scan, find significant lesions, and describe them in the radiology report.
no code implementations • 4 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.
1 code implementation • 18 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.
no code implementations • 13 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.
no code implementations • 11 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.
no code implementations • 18 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.
no code implementations • 2 Jul 2018 • Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes.
4 code implementations • 25 Jun 2018 • Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
3D context is known to be helpful in this differentiation task.
Ranked #10 on Medical Object Detection on DeepLesion
no code implementations • 25 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.
no code implementations • CVPR 2018 • Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhad Bagheri, Ronald Summers
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.
1 code implementation • 4 Oct 2017 • Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers
We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
no code implementations • ICCV 2017 • Ke Yan, Yonghong Tian, Yao-Wei Wang, Wei Zeng, Tiejun Huang
In this paper, we model the relationship of vehicle images as multiple grains.
no code implementations • 12 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.
1 code implementation • 8 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.
2 code implementations • 12 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.
no code implementations • 21 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.
2 code implementations • 15 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.