4 citations · 20 across the 23 of their papers we have counts for
23 papers
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Han Li, Jingsong Liu, Ayako Ura +14
Uterine diseases represent an important category of gynecologic pathology and require accurate histopathological assessment for diagnosis and treatment planning. Whole-slide images…
Clinical Priors Guided Lung Disease Detection in 3D CT Scans
Kejin Lu, Jianfa Bai, Qingqiu Li +5
Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe…
Vision-Language Model Based Multi-Expert Fusion for CT Image Classification
Jianfa Bai, Kejin Lu, Runtian Yuan +5
Robust detection of COVID-19 from chest CT remains challenging in multi-institutional settings due to substantial source shift, source imbalance, and hidden test-source identities.…
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou +13
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with lang…
Advancing Lung Disease Diagnosis in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou +4
To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and re…
Multi-Source COVID-19 Detection via Variance Risk Extrapolation
Runtian Yuan, Qingqiu Li, Junlin Hou +4
We present our solution for the Multi-Source COVID-19 Detection Challenge, which aims to classify chest CT scans into COVID and Non-COVID categories across data collected from four…
EgoExo-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos
Jilan Xu, Yifei Huang, Baoqi Pei +6
Generating videos in the first-person perspective has broad application prospects in the field of augmented reality and embodied intelligence. In this work, we explore the cross-vi…
Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract
Fan Xiao, Junlin Hou, Ruiwei Zhao +5
Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes. As two different imaging tools for DR grading, color fundus photography (…
MOSMOS: Multi-organ segmentation facilitated by medical report supervision
Weiwei Tian, Xinyu Huang, Junlin Hou +6
Owing to a large amount of multi-modal data in modern medical systems, such as medical images and reports, Medical Vision-Language Pre-training (Med-VLP) has demonstrated incredibl…
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
Junlin Hou, Jilan Xu, Hao Chen
The black-box nature of deep learning models has raised concerns about their interpretability for successful deployment in real-world clinical applications. To address the concerns…
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Junlin Hou, Jilan Xu, Rui Feng +1
Due to the complexity of medical image acquisition and the difficulty of annotation, medical image datasets inevitably contain noise. Noisy data with wrong labels affects the robus…
Advancing COVID-19 Detection in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou +4
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung part…
Domain Adaptation Using Pseudo Labels for COVID-19 Detection
Runtian Yuan, Qingqiu Li, Junlin Hou +4
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to…
Retrieval-Augmented Egocentric Video Captioning
Jilan Xu, Yifei Huang, Junlin Hou +4
Understanding human actions from videos of first-person view poses significant challenges. Most prior approaches explore representation learning on egocentric videos only, while ov…
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Bo Qian, Hao Chen, Xiangning Wang +18
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomog…
Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision
Jilan Xu, Junlin Hou, Yuejie Zhang +4
In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categor…
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification
Junlin Hou, Jilan Xu, Nan Zhang +3
This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 ches…
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images
Junlin Hou, Jilan Xu, Fan Xiao +6
Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally…
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
Junlin Hou, Jilan Xu, Nan Zhang +4
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In ou…
Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images
Junlin Hou, Fan Xiao, Jilan Xu +3
The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches fo…
FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis
Junlin Hou, Jilan Xu, Rui Feng +1
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). In ou…
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Shuaicheng Li, Feng Zhang, Rui-Wei Zhao +4
It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed video…
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
Jilan Xu, Junlin Hou, Yuejie Zhang +5
Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a clas…