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Luyang Luo

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4 papers

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AAAI Conference 2026 Conference Paper

Knowledge-Enhanced Explainable Prompting for Vision-Language Models

Yequan Bie
Andong Tan
Zhixuan Chen
Zhiyuan Cai
Luyang Luo
Hao Chen

Large-scale vision-language models (VLMs) embedded with expansive representations and visual concepts have showcased significant potential in image and text understanding. Efficiently adapting VLMs such as CLIP to downstream tasks like few-shot image classification has garnered growing attention, with prompt learning emerging as a representative approach. However, most existing prompt-based adaptation methods, which rely solely on coarse-grained textual prompts, suffer from limited performance and interpretability when handling domain tasks that require specific knowledge. This results in a failure to satisfy the stringent trustworthiness requirements of Explainable Artificial Intelligence (XAI) in high-risk scenarios like healthcare. To address this issue, we propose a Knowledge-Enhanced Explainable Prompting (KEEP) framework that leverages fine-grained domain-specific knowledge to enhance the adaptation process of VLMs across various domains and image modalities. By incorporating retrieval augmented generation and domain foundation models, our framework can provide more reliable image-wise knowledge for prompt learning in various domains, alleviating the lack of fine-grained annotations, while offering both visual and textual explanations. Extensive experiments and explainability analyses conducted on eight datasets of different domains and image modalities demonstrate that our method simultaneously achieves superior performance and interpretability, highlighting the effectiveness of the collaboration between foundation models and XAI.

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JBHI Journal 2024 Journal Article

Guest Editorial: Trustworthy Machine Learning for Health Informatics

Luyang Luo
Daguang Xu
Jing Qin
Yueming Jin
Hao Chen

Machine learning (ML), the stem of today's artificial intelligence, has shown significant growth in the field of biomedical and health informatics. On the one hand, ML techniques are becoming more complex in order to deal with real-world data. On the other hand, ML is also more and more accessible to broader users. For example, automated machine learning products are enabling users to build their own ML models without writing code [1].

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AAAI Conference 2024 Conference Paper

MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment

Yequan Bie
Luyang Luo
Hao Chen

Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis. However, the stringent trustworthiness requirements intrinsic to the medical field have catalyzed research into the utilization of Explainable Artificial Intelligence (XAI), with a particular focus on concept-based methods. Existing concept-based methods predominantly apply concept annotations from a single perspective (e.g., global level), neglecting the nuanced semantic relationships between sub-regions and concepts embedded within medical images. This leads to underutilization of the valuable medical information and may cause models to fall short in harmoniously balancing interpretability and performance when employing inherently interpretable architectures such as Concept Bottlenecks. To mitigate these shortcomings, we propose a multi-modal explainable disease diagnosis framework that meticulously aligns medical images and clinical-related concepts semantically at multiple strata, encompassing the image level, token level, and concept level. Moreover, our method allows for model intervention and offers both textual and visual explanations in terms of human-interpretable concepts. Experimental results on three skin image datasets demonstrate that our method, while preserving model interpretability, attains high performance and label efficiency for concept detection and disease diagnosis. The code is available at https://github.com/Tommy-Bie/MICA.

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JBHI Journal 2020 Journal Article

UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification

Xi Wang
Fangyao Tang
Hao Chen
Luyang Luo
Ziqi Tang
An-Ran Ran
Carol Y. Cheung
Pheng-Ann Heng

Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult and expensive. How to leverage the volume-level labels to develop a robust classifier is very appealing. In this paper, we propose a weakly supervised deep learning framework with uncertainty estimation to address the macula-related disease classification problem from OCT images with the only volume-level label being available. First, a convolutional neural network (CNN) based instance-level classifier is iteratively refined by using the proposed uncertainty-driven deep multiple instance learning scheme. To our best knowledge, we are the first to incorporate the uncertainty evaluation mechanism into multiple instance learning (MIL) for training a robust instance classifier. The classifier is able to detect suspicious abnormal instances and abstract the corresponding deep embedding with high representation capability simultaneously. Second, a recurrent neural network (RNN) takes instance features from the same bag as input and generates the final bag-level prediction by considering the individually local instance information and globally aggregated bag-level representation. For more comprehensive validation, we built two large diabetic macular edema (DME) OCT datasets from different devices and imaging protocols to evaluate the efficacy of our method, which are composed of 30, 151 B-scans in 1, 396 volumes from 274 patients (Heidelberg-DME dataset) and 38, 976 B-scans in 3, 248 volumes from 490 patients (Triton-DME dataset), respectively. We compare the proposed method with the state-of-the-art approaches, and experimentally demonstrate that our method is superior to alternative methods, achieving volume-level accuracy, F1-score and area under the receiver operating characteristic curve (AUC) of 95. 1%, 0. 939 and 0. 990 on Heidelberg-DME and those of 95. 1%, 0. 935 and 0. 986 on Triton-DME, respectively. Furthermore, the proposed method also yields competitive results on another public age-related macular degeneration OCT dataset, indicating the high potential as an effective screening tool in the clinical practice.

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