Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Efficient convolutional neural network (CNN) architecture designs have
attracted growing research interests. However, they usually apply single
receptive field (RF), small asymmetric RFs, or pyramid RFs to learn different
feature representations, still encountering two significant challenges in
medical image classification tasks: 1) They have limitations in capturing
diverse lesion characteristics efficiently, e.g., tiny, coordination, small and
salient, which have unique roles on results, especially imbalanced medical
image classification. 2) The predictions generated by those CNNs are often
unfair/biased, bringing a high risk by employing them to real-world medical
diagnosis conditions. To tackle these issues, we develop a new concept,
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields
(ERoHPRF), to simultaneously boost medical image classification performance and
fairness. This concept aims to mimic the multi-expert consultation mode by
applying the well-designed heterogeneous pyramid RF bags to capture different
lesion characteristics effectively via convolution operations with multiple
heterogeneous kernel sizes. Additionally, ERoHPRF introduces an expert-like
structural reparameterization technique to merge its parameters with the
two-stage strategy, ensuring competitive computation cost and inference speed
through comparisons to a single RF. To manifest the effectiveness and
generalization ability of ERoHPRF, we incorporate it into mainstream efficient
CNN architectures. The extensive experiments show that our method maintains a
better trade-off than state-of-the-art methods in terms of medical image
classification, fairness, and computation overhead. The codes of this paper
will be released soon.
