Publications (119)
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Jiawei Zhao, Ke Yan, Yifan Zhao +3
The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsist…
Deep Learning for Image Denoising: A Survey
Chunwei Tian, Yong Xu, Lunke Fei +1
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 fi…
Information Design in Crowdfunding under Thresholding Policies
Wen Shen, Jacob W. Crandall, Ke Yan +1
Crowdfunding has emerged as a prominent way for entrepreneurs to secure funding without sophisticated intermediation. In crowdfunding, an entrepreneur often has to decide how to di…
Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives
Ke Yan, Wen Shen, Huijuan Lu +1
Fast development of sharing services has become a crucial part of the cyber-enabled world construction process, as sharing services reinvent how people exchange and obtain goods or…
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
Jinzheng Cai, Youbao Tang, Ke Yan +4
Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to th…
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios
Jingyang Lin, Yingda Xia, Jianpeng Zhang +5
3D medical vision-language (VL) pretraining has shown potential in radiology by leveraging large-scale multimodal datasets with CT-report pairs. However, existing methods primarily…
Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning
Bowen Li, Dar-In Tai, Ke Yan +7
Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated…
Location-free Human Pose Estimation
Xixia Xu, Yingguo Gao, Ke Yan +2
Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained anno…
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
Zijiang Yang, Zhongwei Qiu, Tiancheng Lin +13
It is clinically crucial and potentially very beneficial to be able to analyze and model directly the spatial distributions of cells in histopathology whole slide images (WSI). How…
D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning
Evelyn Zhang, Fufu Yu, Aoqi Wu +5
Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we f…
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models
Linglan Zhao, Xuerui Zhang, Ke Yan +2
Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), th…
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data
Wei Fang, Yuxing Tang, Heng Guo +9
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. The lowered…
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding
Yi Xin, Junlong Du, Qiang Wang +2
Large-scale pre-trained models have achieved remarkable success in various computer vision tasks. A standard approach to leverage these models is to fine-tune all model parameters…
AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models
Ziyin Zhou, Yunpeng Luo, Yuanchen Wu +7
The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to…
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
Xiaoyu Bai, Fan Bai, Xiaofei Huo +5
Identifying specific anatomical structures (\textit{e.g.}, lesions or landmarks) in medical images plays a fundamental role in medical image analysis. Exemplar-based landmark detec…
Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
Zhuotun Zhu, Dakai Jin, Ke Yan +7
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are…
A critical review of methods and challenges in large language models
Milad Moradi, Ke Yan, David Colwell +2
This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodol…
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D seg…
Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Bowen Li, Ke Yan, Dar-In Tai +4
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep…
One Click Lesion RECIST Measurement and Segmentation on CT Scans
Youbao Tang, Ke Yan, Jing Xiao +1
In clinical trials, one of the radiologists' routine work is to measure tumor sizes on medical images using the RECIST criteria (Response Evaluation Criteria In Solid Tumors). Howe…
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Youbao Tang, Jinzheng Cai, Le Lu +5
Automated lesion segmentation from computed tomography (CT) is an important and challenging task in medical image analysis. While many advancements have been made, there is room fo…
INR-Based Generative Steganography by Point Cloud Representation
Zhong Yangjie, Liu Jia, Luo Peng +2
Generative steganography (GS) directly generates stego-media through secret message-driven generation. It makes the hiding capacity of GS higher than that of traditional steganogra…
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Zhuotun Zhu, Ke Yan, Dakai Jin +9
Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of on…
VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference
Pengfei Jiang, Hanjun Li, Linglan Zhao +4
In this study, we introduce a novel method called group-wise \textbf{VI}sual token \textbf{S}election and \textbf{A}ggregation (VISA) to address the issue of inefficient inference…
Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
Qiang Wang, Junlong Du, Ke Yan +1
The Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training and has applied to many downstream tasks. We explore the ada…
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Xiaoshuang Chen, Zhongyi Sun, Ke Yan +2
Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classe…
Multi-modal user interface control detection using cross-attention
Milad Moradi, Ke Yan, David Colwell +2
Detecting user interface (UI) controls from software screenshots is a critical task for automated testing, accessibility, and software analytics, yet it remains challenging due to…
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively…
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng +7
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and t…
Predictive Regularization Against Visual Representation Degradation in Multimodal Large Language Models
Enguang Wang, Qiang Wang, Yuanchen Wu +5
While Multimodal Large Language Models (MLLMs) excel at vision-language tasks, the cost of their language-driven training on internal visual foundational competence remains unclear…
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
Ke Yan, Jinzheng Cai, Adam P. Harrison +3
Lesion detection is an important problem within medical imaging analysis. Most previous work focuses on detecting and segmenting a specialized category of lesions (e.g., lung nodul…
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
Youbao Tang, Ke Yan, Jinzheng Cai +6
Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly…
PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans
Jiacheng Hao, Xiaoming Zhang, Wei Liu +8
Focal liver lesions (FLL) are common clinical findings during physical examination. Early diagnosis and intervention of liver malignancies are crucial to improving patient survival…
Some geometric correspondences for homothetic navigation
Ming Xu, Vladimir Matveev, Ke Yan +1
In this paper, we provide conceptional explanations for the geodesic and Jacobi field correspondences for homothetic navigation, and then let them guide us to the shortcuts to some…
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning
Yi Xin, Junlong Du, Qiang Wang +2
Multi-Task Learning (MTL) is designed to train multiple correlated tasks simultaneously, thereby enhancing the performance of individual tasks. Typically, a multi-task network stru…
Dual Relation Mining Network for Zero-Shot Learning
Jinwei Han, Yingguo Gao, Zhiwen Lin +4
Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-…
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
Ke Yan, Youbao Tang, Yifan Peng +4
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 describ…
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
Ke Yan, Lu Kou, David Zhang
Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and…
Fuse Before Transfer: Knowledge Fusion for Heterogeneous Distillation
Guopeng Li, Qiang Wang, Ke Yan +3
Most knowledge distillation (KD) methodologies predominantly focus on teacher-student pairs with similar architectures, such as both being convolutional neural networks (CNNs). How…
LaRE: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
Yunpeng Luo, Junlong Du, Ke Yan +1
The evolution of Diffusion Models has dramatically improved image generation quality, making it increasingly difficult to differentiate between real and generated images. This deve…
3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
Detecting lesions from computed tomography (CT) scans is an important but difficult problem because non-lesions and true lesions can appear similar. 3D context is known to be helpf…
DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations
Ke Yan, Xiaosong Wang, Le Lu +1
Extracting, harvesting and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. It is also the bottleneck to designing more ef…
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Tony C. W. Mok, Zi Li, Yunhao Bai +9
Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-gu…
Expanding Low-Density Latent Regions for Open-Set Object Detection
Jiaming Han, Yuqiang Ren, Jian Ding +3
Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categori…
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
Ke Yan, Jinzheng Cai, Youjing Zheng +7
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often eit…
Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception
Yuanchen Wu, Lu Zhang, Hang Yao +5
Large Vision-Language Models (LVLMs) have achieved impressive results across various cross-modal tasks. However, hallucinations, i.e., the models generating counterfactual response…
Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari Games
Ke Yan
This work analyses the disparity in performance between Decision Transformer (DT) and Decision Mamba (DM) in sequence modelling reinforcement learning tasks for different Atari gam…
Surface mobility gradient and emergent facilitation in glassy films
Qiang Zhai, Xin-Yuan Gao, Chun-Shing Lee +5
Confining glassy polymer into films can substantially modify their local and film-averaged properties. We present a lattice model of film geometry with void-mediated facilitation b…
SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation
Fan Bai, Ke Yan, Xiaoyu Bai +6
Medical image analysis using deep learning is often challenged by limited labeled data and high annotation costs. Fine-tuning the entire network in label-limited scenarios can lead…
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Ke Yan, Xiaosong Wang, Le Lu +4
Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over ye…
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Didi Zhu, Zhongyi Sun, Zexi Li +5
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 sig…
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
Ke Yan, Qing Cai, Fan Zhang +2
Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and…
From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans
Qinji Yu, Yirui Wang, Ke Yan +11
Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatte…
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Mingze Yuan, Yingda Xia, Hexin Dong +13
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically signi…
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Hanjun Li, Xingjia Pan, Ke Yan +2
Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instan…
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
Ke Yan, Jinzheng Cai, Dakai Jin +7
Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying…
A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation
Minfeng Xu, Heng Guo, Jianfeng Zhang +2
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 vari…
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Zhanghexuan Ji, Dazhou Guo, Puyang Wang +9
Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting…
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
Youbao Tang, Ke Yan, Yuxing Tang +3
Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. inten…
MUSE: Multi-Scale Dense Self-Distillation for Nucleus Detection and Classification
Zijiang Yang, Hanqing Chao, Bokai Zhao +10
Nucleus detection and classification (NDC) in histopathology analysis is a fundamental task that underpins a wide range of high-level pathology applications. However, existing meth…
Incomplete Information Linear-Quadratic Mean-Field Games and Related Riccati Equations
Min Li, Tianyang Nie, Shunjun Wang +1
We study a class of linear-quadratic mean-field games with incomplete information. For each agent, the state is given by a linear forward stochastic differential equation with comm…
Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images
Xiaoyu Bai, Fan Bai, Xiaofei Huo +10
Radiotherapists require accurate registration of MR/CT images to effectively use information from both modalities. In a typical registration pipeline, rigid or affine transformatio…
Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings
Bowen Li, Xinping Ren, Ke Yan +6
Depending on the application, radiological diagnoses can be associated with high inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD) solutions treat such data…
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Jinzheng Cai, Ke Yan, Chi-Tung Cheng +4
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/…
Heterogeneous Relational Complement for Vehicle Re-identification
Jiajian Zhao, Yifan Zhao, Jia Li +2
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 vi…
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
Lin Tian, Zi Li, Fengze Liu +7
Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locatio…
Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification
Ke Yan, Dakai Jin, Dazhou Guo +5
Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are…
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis
Yankai Jiang, Mingze Sun, Heng Guo +4
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image analysis tasks. Most current methods follow existing SSL paradigm originally designe…
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
Jieneng Chen, Ke Yan, Yu-Dong Zhang +9
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tu…
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Yuxi Li, Weiyao Lin, John See +4
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 explo…
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo +10
Determining the spread of GTV is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy fo…
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
Ke Yan, Yifan Peng, Zhiyong Lu +1
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and write sentences in the radiology report to describe them. I…
Discriminator-Free Generative Adversarial Attack
Shaohao Lu, Yuqiao Xian, Ke Yan +5
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of t…
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
Ke Yan, Le Lu, Ronald M. Summers
Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement o…
ITPP: Learning Disentangled Event Dynamics in Marked Temporal Point Processes
Wang-Tao Zhou, Zhao Kang, Ke Yan +1
Marked Temporal Point Processes (MTPPs) provide a principled framework for modeling asynchronous event sequences by conditioning on the history of past events. However, most existi…
A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans
Fakai Wang, Chi-Tung Cheng, Chien-Wei Peng +5
Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatic…
Vision-Language Models for Automated 3D PET/CT Report Generation
Wenpei Jiao, Kun Shang, Hui Li +8
Positron emission tomography/computed tomography (PET/CT) is essential in oncology, yet the rapid expansion of scanners has outpaced the availability of trained specialists, making…
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
Fengze Liu, Ke Yan, Adam Harrison +8
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of compu…
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Yi Luo, Aiguo Chen, Ke Yan +1
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data. Models in this paradigm have to spend extra space to…
HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
Yi Huang, Ke Zhang, Wei Liu +6
Accurate segmentation of tubular structures in medical images, such as vessels and airway trees, is crucial for computer-aided diagnosis, radiotherapy, and surgical planning. Howev…
Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
Jinzheng Cai, Adam P. Harrison, Youjing Zheng +5
Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent d…
Model-agnostic explainable artificial intelligence for object detection in image data
Milad Moradi, Ke Yan, David Colwell +2
In recent years, deep neural networks have been widely used for building high-performance Artificial Intelligence (AI) systems for computer vision applications. Object detection is…
Integrating high-quality dielectrics with one-nanometer equivalent oxide thickness on two-dimensional electronic devices
Weisheng Li, Jian Zhou, Songhua Cai +21
Two-dimensional (2D) semiconductors are widely recognized as attractive channel materials for low-power electronics. However, an unresolved challenge is the integration of high-qua…
TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification
Zishuo Wan, Qinqin Kang, Na Li +6
The accurate diagnosis and segmentation of tumors in contrast-enhanced Computed Tomography (CT) are fundamentally driven by the distinctive hemodynamic profiles of contrast agents…
An empirical study of LoRA-based fine-tuning of large language models for automated test case generation
Milad Moradi, Ke Yan, David Colwell +1
Automated test case generation from natural language requirements remains a challenging problem in software engineering due to the ambiguity of requirements and the need to produce…
Image steganography based on generative implicit neural representation
Zhong Yangjie, Liu Jia, Ke Yan +1
In the realm of advanced steganography, the scale of the model typically correlates directly with the resolution of the fundamental grid, necessitating the training of a distinct n…
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images
Zhongwei Qiu, Hanqing Chao, Wenbin Liu +6
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide imag…
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Yirui Wang, Qinji Yu, Ke Yan +9
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for…
Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
Yuanchen Wu, Ke Yan, Shouhong Ding +2
Large Vision-Language Models (LVLMs) have manifested strong visual question answering capability. However, they still struggle with aligning the rationale and the generated answer,…
ToVE: Efficient Vision-Language Learning via Knowledge Transfer from Vision Experts
Yuanchen Wu, Junlong Du, Ke Yan +2
Vision-language (VL) learning requires extensive visual perception capabilities, such as fine-grained object recognition and spatial perception. Recent works typically rely on trai…
Learning a Repression Network for Precise Vehicle Search
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. Precise vehicle search, ai…
A self-attention based deep learning method for lesion attribute detection from CT reports
Yifan Peng, Ke Yan, Veit Sandfort +2
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.…
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
Zhongwei Qiu, Hanqing Chao, Tiancheng Lin +12
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, th…
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
Wei Huang, Wei Liu, Xiaoming Zhang +9
The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability o…
Improved Esophageal Varices Assessment from Non-Contrast CT Scans
Chunli Li, Xiaoming Zhang, Yuan Gao +5
Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures. Despite non-contrast compu…
Non-Contrast CT Esophageal Varices Grading through Clinical Prior-Enhanced Multi-Organ Analysis
Xiaoming Zhang, Chunli Li, Jiacheng Hao +9
Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of ~30%. While…
Anchor-based Robust Finetuning of Vision-Language Models
Jinwei Han, Zhiwen Lin, Zhongyisun Sun +5
We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as…
Few-Shot Object Detection via Variational Feature Aggregation
Jiaming Han, Yuqiang Ren, Jian Ding +2
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 sensit…
A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT
Dazhou Guo, Zhanghexuan Ji, Yanzhou Su +31
Precision medicine in the quantitative management of chronic diseases and oncology would be greatly improved if the Computed Tomography (CT) scan of any patient could be segmented,…
HODN: Disentangling Human-Object Feature for HOI Detection
Shuman Fang, Zhiwen Lin, Ke Yan +3
The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances cur…