Abstract

The intricate nature of multi-class histopathological images, combined with pronounced class imbalances, complicates automated breast cancer diagnosis and demands AI models capable of generalizing well beyond often limited training data. To address these challenges, this paper explores the generative modeling capability of a symmetry-driven enhanced auxiliary classifier GAN (LSWACGAN) as an all-in-one, data-efficient framework for breast cancer histopathological image classification. LSWACGAN incorporates the Wasserstein loss with gradient penalty to promote greater training stability by mitigating overfitting and preventing vanishing gradients. Assigning smooth category labels to generated samples further helps alleviate the mode collapse problem. The proposed framework brings together three types of symmetry to improve its reliability: the inherent metric symmetry of the Wasserstein distance, the structural symmetry within the auxiliary classifier GAN, and the architectural symmetry between the generator and discriminator networks. Extensive experiments conducted on the well-known BreakHis dataset, supplemented by a thorough ablation study, demonstrate the framework’s competitive edge in a lower-data regime. For binary classification, LSWACGAN closely matches or slightly outperforms leading benchmarks on most selected evaluation metrics. Conversely, in the multi-class scenario, it emerges as a clear forerunner, consistently producing superior results and maintaining robust performance across varying magnification levels.

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