Abstract
This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell carcinoma (ccRCC) images, a diagnostic task traditionally reliant on subjective visual grading by pathologists. We evaluate various AE architectures, including standard AEs, contractive AEs (CAEs), and discriminative AEs (DAEs), as well as a classifier-based discriminative AE (CDAE), optimized using the hyperparameter tuning tool Optuna. Bhattacharyya distance is selected from several metrics to assess class separability in the latent space, revealing challenges in distinguishing adjacent grades using unsupervised models. CDAE, integrating a supervised classifier branch, demonstrated superior performance in both latent space separation and classification accuracy. Given that CDAE-CNN achieved notable improvements in classification metrics, affirming the value of supervised learning for class-specific feature extraction, F1 score was incorporated into the tuning process to optimize classification performance. Results show significant improvements in identifying aggressive ccRCC grades by leveraging the classification capability of AE through latent clustering followed by fine-grained classification. Our model outperforms the current state of the art, CHR-Network, across all evaluated metrics. These findings suggest that integrating a classifier branch in AEs, combined with neural architecture search and contrastive learning, enhances grading automation in ccRCC pathology, particularly in detecting aggressive tumor grades, and may improve diagnostic accuracy.
Abstract (translated)
这项研究探讨了监督式和非监督式自动编码器（AEs）在透明细胞肾细胞癌（ccRCC）图像中自动化核分类的应用，这是一个传统上依赖病理学家主观视觉评估的诊断任务。我们评估了各种AE架构，包括标准AE、收缩AE（CAE）、判别性AE（DAE），以及使用超参数优化工具Optuna优化的基于分类器的判别性AE（CDAE）。从多个度量中选择了Bhattacharyya距离来评估潜在空间中的类分离情况，揭示了使用无监督模型区分相邻等级时面临的挑战。集成有监督分类分支的CDAE在潜在空间分离和分类准确性方面表现出色。 鉴于CDAE-CNN在分类指标上取得了显著改进，证实了针对特定类别特征提取的监督学习的价值，F1分数被纳入到优化过程中以提高分类性能。结果表明，通过使用自动编码器进行潜在聚类后紧随其后的精细分类能力，在识别侵袭性ccRCC等级方面有了明显的改善。我们的模型在所有评估指标上都优于当前最先进的CHR-Network。这些发现表明，结合神经架构搜索和对比学习的自动编码器中的分类分支，可以提高ccRCC病理学中分级自动化的能力，特别是在检测侵袭性肿瘤等级方面，并可能提高诊断准确性。
URL
https://arxiv.org/abs/2504.03146
