Xueshen Li

Xueshen Li, Shengting Cao, Hongshan Liu et al.

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/ features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale- Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial downscaled data can be better reconstructed than data that is downscaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention.

Xueshen Li, Hongshan Liu, Xiaoyu Song et al.

Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.

Xueshen Li, Hongshan Liu, Xiaoyu Song et al.

There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease. However, existing methods either require a large pixel-wisely paired training dataset or have limited capability to map pathological regions. To address these issues, we proposed a structural constrained, pathology aware, transformer generative adversarial network, namely SCPAT-GAN, to generate virtual stained H&E histology from OCT images. The proposed SCPAT-GAN advances existing methods via a novel design to impose pathological guidance on structural layers using transformer-based network.

Xueshen Li, Zhenxing Dong, Hongshan Liu et al.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Han Yue, Eric Zhou, Alex Hu et al.

Biomedical question answering (QA) encompasses diverse question types, including yes/no, factoid, list, and summary questions, each requiring distinct forms of evidence and reasoning. However, most retrieval-augmented QA systems rely on a unified retrieval pipeline, regardless of the information needs of different question categories. This one-size-fits-all approach may limit the effectiveness of evidence acquisition and downstream answer generation. In this work, we propose an adaptive retrieval framework that selects retrieval and evidence aggregation strategies according to question type. The system combines query understanding, biomedical document retrieval, reranking, knowledge graph augmentation, document clustering, and large language model-based answer generation. For yes/no questions, it focuses on precise evidence retrieval; for factoid and list questions, it emphasizes entity-oriented retrieval and clustering; and for summary questions, it performs broader evidence collection and synthesis. We evaluate the proposed framework on the BioASQ benchmark and demonstrate that adaptive retrieval strategies improve evidence relevance and answer quality across multiple question types. Our results suggest that aligning retrieval mechanisms with question-specific information needs provides an effective direction for enhancing retrieval-augmented biomedical QA systems.

Xueshen Li, Xinlong Hou, Ziyi Huang et al.

Recent advancements in large language models (LLMs) have demonstrated extraordinary comprehension capabilities with remarkable breakthroughs on various vision-language tasks. However, the application of LLMs in generating reliable medical diagnostic reports remains in the early stages. Currently, medical LLMs typically feature a passive interaction model where doctors respond to patient queries with little or no involvement in analyzing medical images. In contrast, some ChatBots simply respond to predefined queries based on visual inputs, lacking interactive dialogue or consideration of medical history. As such, there is a gap between LLM-generated patient-ChatBot interactions and those occurring in actual patient-doctor consultations. To bridge this gap, we develop an LLM-based dialogue system, namely proactive multi-round vision-language interactions for computer-aided diagnosis (ProMRVL-CAD), to generate patient-friendly disease diagnostic reports. The proposed ProMRVL-CAD system allows proactive dialogue to provide patients with constant and reliable medical access via an integration of knowledge graph into a recommendation system. Specifically, we devise two generators: a Proactive Question Generator (Pro-Q Gen) to generate proactive questions that guide the diagnostic procedure and a Multi-Vision Patient-Text Diagnostic Report Generator (MVP-DR Gen) to produce high-quality diagnostic reports. Evaluating two real-world publicly available datasets, MIMIC-CXR and IU-Xray, our model has better quality in generating medical reports. We further demonstrate the performance of ProMRVL achieves robust under the scenarios with low image quality. Moreover, we have created a synthetic medical dialogue dataset that simulates proactive diagnostic interactions between patients and doctors, serving as a valuable resource for training LLM.