Mani-NENet: Manifold Neuron Energy Network-Enhancing Dysplastic Cell Perception for Lung Adenocarcinoma Subtype Recognition

Dysplastic cells are vital for diagnosing Subtypes of Lung Adenocarcinoma (SLA) but are difficult to classify due to high morphological similarity. Existing models often suffer from a feature coherency problem, failing to effectively align local dysplastic details with global subtype representations. To address this challenge, we propose Mani-NENet, a manifold neuron energy network-enhancing dysplastic cells perception built on three core innovations. First, we design a Dysplastic Perception Block (DPB) with a novel neuron energy function that prioritizes highly discriminative cell populations, inspired by the similarity between neuronal activation mechanisms and the diagnostic process. Second, we introduce a Differentiated Representation Learning Block (DRLB) in manifold space to amplify subtype-specific features and mitigate the challenge of high intersubtype similarity. Third, we propose a Key Patch Block (KPB) to enhance the extraction of low-level image features, capturing fine-grained morphological details of dysplastic cells. Extensive experiments on the AMU-SLA and AMU-BC datasets demonstrate that Mani-NENet outperforms other 19 state-of-the-art methods, achieving top-ranking average accuracies of 89.21% and 94.71%, respectively. The ablation results demonstrate that our classification performance improves by 1.91%, 2.42%, and 5.54% after applying DPB, DRLB, and KPB, respectively. In addition, we analyze the classification performance of each subclass and achieve accuracies of 94.64%, 96.82%, 97.49%, and 98.66% for LIA, AAH, MPIA, and PIA, respectively. Qualitative analysis shows accurate localization of dysplastic regions, demonstrating strong clinical interpretability and potential for human–machine collaboration.