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QiHao Xu

7 accepted papers

2026

Frequency-Aligned Cross-Modal Learning with Top-K Wavelet Fusion and Dynamic Expert Routing for Enhanced Retinal Disease Diagnosis

AAAI 2026technical

Multimodal fusion of color fundus photography (CFP) and optical coherence tomography (OCT) B-scan images has demonstrated superior diagnostic potential for retinal diseases compared to single-modality approaches. However, existing fusion paradigms - whether through naive concatenation or attention m

Cited by 0SourcePDFScholar
2026

ProConMV: Provenance-Enabled Conceptual Framework for Interpretable Multi-View Diabetic Retinopathy Diagnosis

ICML 2026poster

Existing deep learning models have demonstrated potential in Diabetic retinopathy (DR) diagnosis, but they still suffer from three key challenges: reliance on single-source inputs, opaque and untraceable reasoning processes, and the absence of a mechanism for result verification. Thus, we propose a …

Cited by 0SourceScholar
2026

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

AAAI 2026technical

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fun

Cited by 0SourcePDFScholar
2026

Vision-Language Models Guided Graph Concept Reasoning for Interpretable Diabetic Retinopathy Diagnosis

AAAI 2026technical

Deep neural networks (DNNs) have significantly advanced diabetic retinopathy (DR) diagnosis, yet their black-box nature limits clinical acceptance due to a lack of interpretability. Concept bottleneck model (CBM) offers a promising solution by enabling concept-level reasoning and test-time intervent

Cited by 0SourcePDFScholar
2025

Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease Diagnosis

NeurIPS 2025poster

Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehen…

Cited by 0SourceScholar
2025

Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading

AAAI 2025technical

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different f…

2024

HACDR-Net: Heterogeneous-Aware Convolutional Network for Diabetic Retinopathy Multi-Lesion Segmentation

AAAI 2024technical

Diabetic Retinopathy (DR), the leading cause of blindness in diabetic patients, is diagnosed by the condition of retinal multiple lesions. As a difficult task in medical image segmentation, DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in loca…