Savchenko Taras, Lakhtaryna Ruslana, Denysenko Anastasiia, Dovbysh Anatoliy, Coupland Sarah E, Moskalenko Roman
Department of Computer Science, Sumy State University, 40000 Sumy, Ukraine.
Department of Pathology, Sumy State University, 40000 Sumy, Ukraine.
Diagnostics (Basel). 2025 May 30;15(11):1389. doi: 10.3390/diagnostics15111389.
Breast cancer diagnosis heavily relies on histopathological assessment, which is prone to subjectivity and inefficiency, especially with whole-slide imaging (WSI). This study addressed these limitations by developing an automated breast cancer cell classification algorithm using an information-extreme machine learning approach and universal cytological features, aiming for objective and generalized histopathological diagnosis. : Digitized histological images were processed to identify hyperchromatic cells. A set of 21 cytological features (10 geometric and 11 textural), chosen for their potential universality across cancers, were extracted from individual cells. These features were then used to classify cells as normal or malignant using an information-extreme algorithm. This algorithm optimizes an information criterion within a binary Hamming space to achieve robust recognition with minimal input features. The architectural innovation lies in the application of this information-extreme approach to cytological feature analysis for cancer cell classification. : The algorithm's functional efficiency was evaluated on a dataset of 176 labeled cell images, yielding promising results: an accuracy of 89%, a precision of 85%, a recall of 84%, and an F1-score of 88%. These metrics demonstrate a balanced and effective model for automated breast cancer cell classification. : The proposed information-extreme algorithm utilizing universal cytological features offers a potentially objective and computationally efficient alternative to traditional methods and may mitigate some limitations of deep learning in histopathological analysis. Future work will focus on validating the algorithm on larger datasets and exploring its applicability to other cancer types.
乳腺癌诊断在很大程度上依赖于组织病理学评估，而这种评估容易受到主观性和低效率的影响，尤其是在全切片成像（WSI）方面。本研究通过使用信息极值机器学习方法和通用细胞学特征开发一种自动乳腺癌细胞分类算法，解决了这些局限性，旨在实现客观和通用的组织病理学诊断。：对数字化组织学图像进行处理以识别核深染细胞。从单个细胞中提取了一组21个细胞学特征（10个几何特征和11个纹理特征），这些特征因其在各种癌症中的潜在通用性而被选中。然后使用信息极值算法将这些特征用于将细胞分类为正常或恶性。该算法在二元汉明空间内优化信息准则，以使用最少的输入特征实现稳健识别。其架构创新在于将这种信息极值方法应用于癌细胞分类的细胞学特征分析。：在一个包含176个标记细胞图像的数据集上评估了该算法的功能效率，结果令人满意：准确率为89%，精确率为85%，召回率为84%，F1分数为88%。这些指标表明该模型对于自动乳腺癌细胞分类是平衡且有效的。：所提出的利用通用细胞学特征的信息极值算法为传统方法提供了一种潜在的客观且计算高效的替代方案，并且可能减轻深度学习在组织病理学分析中的一些局限性。未来的工作将集中在更大的数据集上验证该算法，并探索其对其他癌症类型的适用性。
