Parameterized Diffusion Optimization enabled Autoregressive Ordinal Regression for Diabetic Retinopathy Grading
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
As a long-term complication of diabetes, diabetic retinopathy (DR) progresses
slowly, potentially taking years to threaten vision. An accurate and robust
evaluation of its severity is vital to ensure prompt management and care.
Ordinal regression leverages the underlying inherent order between categories
to achieve superior performance beyond traditional classification. However,
there exist challenges leading to lower DR classification performance: 1) The
uneven distribution of DR severity levels, characterized by a long-tailed
pattern, adds complexity to the grading process. 2)The ambiguity in defining
category boundaries introduces additional challenges, making the classification
process more complex and prone to inconsistencies. This work proposes a novel
autoregressive ordinal regression method called AOR-DR to address the above
challenges by leveraging the clinical knowledge of inherent ordinal information
in DR grading dataset settings. Specifically, we decompose the DR grading task
into a series of ordered steps by fusing the prediction of the previous steps
with extracted image features as conditions for the current prediction step.
Additionally, we exploit the diffusion process to facilitate conditional
probability modeling, enabling the direct use of continuous global image
features for autoregression without relearning contextual information from
patch-level features. This ensures the effectiveness of the autoregressive
process and leverages the capabilities of pre-trained large-scale foundation
models. Extensive experiments were conducted on four large-scale publicly
available color fundus datasets, demonstrating our model's effectiveness and
superior performance over six recent state-of-the-art ordinal regression
methods. The implementation code is available at
https://github.com/Qinkaiyu/AOR-DR.
