15 papers
MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark
Junzhi Ning, Jiashi Lin, Yingying Fang +9
Multimodal large language models (MLLMs) have advanced clinical tasks for common conditions, but their performance on rare diseases remains largely untested. In rare-disease scenar…
Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning
Kinhei Lee, Peiyuan Jing, Zhenxuan Zhang +5
Large scale vision language models have shown promise in automating chest Xray interpretation, yet their clinical utility remains limited by a gap between model outputs and radiolo…
Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks
Rubén Moreno-Aguado, Alba Magallón, Victor Moreno +2
There is substantial interest in developing artificial intelligence systems to support radiologists across tasks ranging from segmentation to report generation. Existing computed t…
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
Zhiling Yue, Yingying Fang, Liutao Yang +3
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for…
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Shiyi Wang, Yang Nan, Sheng Zhang +6
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face challenges: the opaci…
Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
Sheng Zhang, Yang Nan, Yingying Fang +4
Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negat…
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation
Yingying Fang, Shuang Wu, Zihao Jin +4
In the field of medical imaging, particularly in tasks related to early disease detection and prognosis, understanding the reasoning behind AI model predictions is imperative for a…
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
Zihao Jin, Yingying Fang, Jiahao Huang +3
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for me…
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery
Yingying Fang, Zihao Jin, Xiaodan Xing +2
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability…
Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants
Shiyi Wang, Yang Nan, Felder Federico N +3
Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be obtained. The most popular…
Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
Xiaodan Xing, Huiyu Zhou, Yingying Fang +1
AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identific…
Dynamic Multimodal Information Bottleneck for Multimodality Classification
Yingying Fang, Shuang Wu, Sheng Zhang +5
Effectively leveraging multimodal data such as various images, laboratory tests and clinical information is gaining traction in a variety of AI-based medical diagnosis and prognosi…
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
Yingying Fang, Xiaodan Xing, Shiyi Wang +2
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to…
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation
Shiyi Wang, Yang Nan, Simon Walsh +1
We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmen…
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
Xiaodan Xing, Chunling Tang, Yunzhe Guo +2
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studyin…