Jin Junwei, Zhou Songbo, Li Yanting, Zhu Tanxin, Fan Chao, Zhang Hua, Li Peng
The Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou, 450001, China.
Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Henan University of Technology, Zhengzhou, 450001, China.
Interdiscip Sci. 2025 Mar;17(1):215-230. doi: 10.1007/s12539-024-00683-2. Epub 2025 Jan 22.
Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies among different diseases and the diversity of pathologies within the same disease. To address this issue, this paper proposes a reinforced collaborative-competitive representation classification (RCCRC) method. RCCRC enhances the contribution of different classes by introducing dual competitive constraints into the objective function. The first constraint integrates the collaborative space representation akin to holistic data, promoting the representation contribution of similar classes. The second constraint introduces specific class subspace representations to encourage competition among all classes, enhancing the discriminative nature of representation vectors. By unifying these two constraints, RCCRC effectively explores both global and specific data features in the reconstruction space. Extensive experiments on various biomedical image databases are conducted to exhibit the advantage of the proposed method in comparison with several state-of-the-art classification algorithms.
人工智能技术在现代生物医学图像分析中已展现出卓越的诊断效能。然而，人工智能的实际应用受到不同疾病间存在相似病理以及同一疾病内病理多样性的显著限制。为解决这一问题，本文提出一种强化协作竞争表示分类（RCCRC）方法。RCCRC通过在目标函数中引入双重竞争约束来增强不同类别的贡献。第一个约束整合类似于整体数据的协作空间表示，促进相似类别的表示贡献。第二个约束引入特定类别的子空间表示以鼓励所有类别间的竞争，增强表示向量的判别性。通过统一这两个约束，RCCRC在重构空间中有效探索了全局和特定数据特征。在各种生物医学图像数据库上进行了广泛实验，以展示所提方法与几种先进分类算法相比的优势。
