8 papers
Meta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference
Haozhe Luo, Nirosh Sivanesan, Shelley Zixin Shu +2
Medical vision--language pretraining learns transferable representations from paired images and radiology reports, yet differences in scanners and acquisition protocols can impair…
HarMoE: Multi-Source Chest Radiograph Pretraining with Dataset-Disentangled Experts
Haozhe Luo, Ziyu Zhou, Shelley Zixin Shu +1
Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the dominant pretraining source.…
KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection
Haozhe Luo, Shelley Zixin Shu, Ziyu Zhou +2
Vision--language models (VLMs) show promise for clinical decision support in radiology because they enable joint reasoning over radiological images and clinical text, thereby lever…
Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation
Sanaz Karimijafarbigloo, Armin Khosravi, Alireza Kheyrkhah +3
Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing app…
XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography
Haozhe Luo, Shelley Zixin Shu, Ziyu Zhou +2
Vision-language models (VLMs) have recently shown remarkable zero-shot performance in medical image understanding, yet their grounding ability, the extent to which textual concepts…
Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis
Shelley Zixin Shu, Haozhe Luo, Alexander Poellinger +1
Transformer-based deep learning models have demonstrated exceptional performance in medical imaging by leveraging attention mechanisms for feature representation and interpretabili…
On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging
Haozhe Luo, Ziyu Zhou, Zixin Shu +3
Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups. We provide the first systematic exploration of Human-A…
DWARF: Disease-weighted network for attention map refinement
Haozhe Luo, Aurélie Pahud de Mortanges, Oana Inel +2
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study a…