Yitian Zhao

Prototype-Decomposed Feature Imputation for Incomplete Multi-Field-of-View and Multi-Projection OCTA Image Classification.

Optical coherence tomography angiography (OCTA) provides high-resolution visualization of fundus microvasculature and plays a crucial role in the diagnosis of fundus diseases. However, in real-world clinical practice, variations in imaging devices and acquisition protocols across centers lead to pronounced heterogeneity in OCTA data across field-of-view (FOV) and projection-layer dimensions, manifested as inconsistent FOV coverage and unavailable FOV-projection inputs. Existing methods predominantly focus on small-FOV images (e.g., $3\times 3$ and $6\times \text{6}~\text{mm}^{2}$), with limited utilization of larger-FOV information, making them less capable of handling data incompleteness and heterogeneity in complex clinical scenarios and thereby limiting their generalization and robustness. To address these issues, this paper presents a prototype-decomposed feature imputation framework for disease classification from incomplete multi-FOV and multi-projection OCTA data. By leveraging prototype-guided feature decomposition and imputation, the proposed framework enables disease classification through the dynamic integration of complementary representations. The framework comprises two stages: feature imputation and feature classification. In the feature-imputation stage, a prototype-based reconstruction network jointly employs contrastive learning and clustering to construct FOV-projection-specific prototypes. This design promotes a compact and discriminative encoded feature space while mitigating representation discrepancies arising from heterogeneous FOVs, projection layers, and imaging devices. Each available encoded feature is subsequently decomposed into a prototype-related shared component and a disease-related residual component, which are used to estimate the representation of each missing FOV-projection input through a non-parametric neighbor-based procedure. In the feature-classification stage, attention-based fusion mechanisms jointly model observed and imputed FOV-projection features across FOVs and projection layers. Furthermore, Dirichlet-based uncertainty modeling is incorporated to quantify the predictive uncertainty of the fused branches, followed by dynamic decision fusion to obtain the final classification result. The proposed framework is evaluated on a retrospective multi-center and multi-device OCTA cohort comprising 5,644 eyes. Comparative experiments with state-of-the-art feature-imputation and incomplete multi-input classification methods demonstrate that the proposed approach effectively estimates the representations of missing FOV-projection inputs and achieves superior performance in fundus disease classification.

A frequency-aware dual-domain collaborative framework for medical image enhancement.

Automated medical image analysis plays a critical role in disease screening and diagnosis. However, image quality degradation, such as blurring and uneven illumination hinders both clinical interpretation and computer-aided diagnostic performance. Most existing enhancement methods often overlook frequency-domain degradation patterns, leading to over-enhancement or loss of clinically relevant details. To address these limitations, we propose a frequency-aware dual-domain collaborative framework for medical image enhancement, termed FDRNet, comprising two key components in this paper: (1) a frequency-decoupled deblurring module with asymmetric channel integration, which combines global and local views using high- and low-frequency information to preserve fine and broad structural features, and (2) a Retinex-guided illumination compensation module with a multi-scale color preservation unit for accurate estimation and correction. Our framework further incorporates a dual-domain collaboration mechanism into each encoder-decoder block of the deblurring module, enabling joint learning of degradation representations in spatial and frequency domains. Extensive experiments on three medical image modalities, utilizing seven public and clinical datasets demonstrate that our method surpasses both traditional and learning-based enhancement techniques. Evaluations on downstream clinical tasks, including vessel segmentation, polyp segmentation, disease diagnosis, and disease severity grading, further confirm significant improvements in task performance and clinical applicability. Our code is available at: https://github.com/iMED-Lab/FDRNet-PyTorch.

Consistency-guided Uncertainty Estimation for Source-Free Medical Image Segmentation.

Medical Image Analysis

Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tomography (PET), or ultrasound (US), offers previously unattained opportunities for diagnosis, therapy planning, and therapy assessment. Medical image processing is essential to leverage this increasing amount of data and to explore and present the contained information in a way suitable for the specific medical task. In this tutorial, we will approach the analysis and visualization of medical image data in an explorative manner. In particular, we will visually construct the image processing algorithms using the popular graphical data-flow builder MeVisLab, which is available as a free download for noncommercial research. We felt that it could be more interesting for the reader to see and explore examples of medical image processing that go beyond simple image enhancements. The part of exploration, to inspect medical image data and experiment with image-processing pipelines, requires software that encourages this kind of visual exploration.

Medical Image Analysis

Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tomography (PET), or ultrasound (US), offers previously unattained opportunities for diagnosis, therapy planning, and therapy assessment. Medical image processing is essential to leverage this increasing amount of data and to explore and present the contained information in a way suitable for the specific medical task. In this tutorial, we will approach the analysis and visualization of medical image data in an explorative manner. In particular, we will visually construct the image processing algorithms using the popular graphical data-flow builder MeVisLab, which is available as a free download for noncommercial research. We felt that it could be more interesting for the reader to see and explore examples of medical image processing that go beyond simple image enhancements. The part of exploration, to inspect medical image data and experiment with image-processing pipelines, requires software that encourages this kind of visual exploration.