MediTEDNet: Visual State Space Model for Thyroid Eye Disease Classification.

Abstract

Thyroid eye disease (TED) is a prevalent autoimmune orbital disorder that can severely impair visual function and significantly diminish patients' quality of life. In recent years, several studies have attempted to automate TED diagnosis using optical coherence tomography (OCT) images. However, existing approaches primarily rely on convolutional neural networks (CNNs) combined with attention mechanisms and are mostly trained using traditional cross-entropy loss. Although Transformers excel at modeling long-range dependencies, their quadratic computational complexity when processing high-resolution medical images, along with subpar classification accuracy in challenging scenarios such as highly similar pathological regions and blurred image boundaries, limit their clinical applicability. To tackle these challenges, we propose a hybrid architecture that integrates CNNs, attention mechanisms, and visual state space models (VSSMs) to enhance the robustness and discriminability of image features. In addition, to achieve intra-class compactness and inter-class separation, we design a contrastive loss based on positive and negative sample prototypes. Specifically, we introduce proximal inter-class mean sampling (PICMS) and incorporate a normalized distance metric guided by a distinguishable-indistinguishable triplet partitioning mechanism. We also introduce a hierarchical noise-resilient training strategy to reduce the effects of noise frequently present in clinical images. To assess the effectiveness of our proposed model, we conduct experiments on two public datasets (OCT-2017 and OCT-C8) and a clinical dataset of TED images. The results reveal that our model outperforms existing methods across multiple evaluation metrics, including accuracy and F1-score while demonstrating superior diagnostic stability and generalization capability.