4 citations · 13 across the 17 of their papers we have counts for
18 papers
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.…
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…
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
Qingqiu Li, Zihang Cui, Seongsu Bae +8
Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Large Multimodal Models (LMMs) have enabled automated CXR in…
Text-Promptable Propagation for Referring Medical Image Sequence Segmentation
Runtian Yuan, Mohan Chen, Jilan Xu +6
Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (\emph{e.g.} endoscop…
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…
Anatomical Structure-Guided Medical Vision-Language Pre-training
Qingqiu Li, Xiaohan Yan, Jilan Xu +6
Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., lo…
Enhanced Knowledge Injection for Radiology Report Generation
Qingqiu Li, Jilan Xu, Runtian Yuan +5
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anoma…
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…
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…