对比多种图像特征，提升胃癌病理切片自动分类准确率
A Comparative Analysis of Image Descriptors for Histopathological Classification of Gastric Cancer
- 用手工特征与深度特征结合浅层分类器进行对比实验
- 随机森林分类器达93.4%的F1分数，效果最佳
- 适合需要无微调方案的病理图像诊断研究者
胃癌是全球第五大常见、第四大致死性癌症，5年生存率仅约20%。尽管对其病理生物学研究广泛，但预后预测能力仍不足，且病理科医生工作量大、易出错。因此，开发自动化、高精度的组织病理学诊断工具至关重要。本研究采用机器学习与深度学习方法，基于GasHisSDB数据集，对正常与癌变组织切片进行分类。通过对比手工特征与深度特征在浅层分类器上的表现，分析最优特征-分类器组合，无需微调策略即可实现高效区分。结果表明，使用随机森林（RF）分类器时，F1分数可达93.4%，验证了该方法的有效性。
原文摘要 · Abstract (English)
Gastric cancer ranks as the fifth most common and fourth most lethal cancer globally, with a dismal 5-year survival rate of approximately 20%. Despite extensive research on its pathobiology, the prognostic predictability remains inadequate, compounded by pathologists' high workload and potential diagnostic errors. Thus, automated, accurate histopathological diagnosis tools are crucial. This study employs Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. Using handcrafted and deep features with shallow learning classifiers on the GasHisSDB dataset, we offer a comparative analysis and insights into the most robust and high-performing combinations of features and classifiers for distinguishing between normal and abnormal histopathological images without fine-tuning strategies. With the RF classifier, our approach can reach F1 of 93.4%, demonstrating its validity.
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