皮肤肿瘤是人类最常见的肿瘤，三种常见非黑色素瘤（IDN、SK、BCC）的临床特征相似，导致误诊率较高。这些肿瘤的准确鉴别诊断需要根据病理图像来判断。然而，由于缺乏经验丰富的皮肤病理学家，导致中国这些皮肤肿瘤的诊断准确性存在偏差。在本文中，我们针对三种非黑色素瘤建立了皮肤病理图像数据集SPMLD，以实现对它们的自动、准确的智能识别。同时，我们提出了一种带有 KLS 模块和注意力模块的基于病变区域的增强分类网络。具体来说，我们首先收集数千个H

Skin tumors are the most common tumors in humans and the clinical characteristics of three common non-melanoma tumors (IDN, SK, BCC) are similar, resulting in a high misdiagnosis rate. The accurate differential diagnosis of these tumors needs to be judged based on pathological images. However, a shortage of experienced dermatological pathologists leads to bias in the diagnostic accuracy of these skin tumors in China. In this paper, we establish a skin pathological image dataset, SPMLD, for three non-melanoma to achieve automatic and accurate intelligent identification for them. Meanwhile, we propose a lesion-area-based enhanced classification network with the KLS module and an attention module. Specifically, we first collect thousands of H&E-stained tissue sections from patients with clinically and pathologically confirmed IDN, SK, and BCC from a single-center hospital. Then, we scan them to construct a pathological image dataset of these three skin tumors. Furthermore, we mark the complete lesion area of the entire pathology image to better learn the pathologist's diagnosis process. In addition, we applied the proposed network for lesion classification prediction on the SPMLD dataset. Finally, we conduct a series of experiments to demonstrate that this annotation and our network can effectively improve the classification results of various networks. The source dataset and code are available at https://github.com/efss24/SPMLD.git.Copyright © 2024 Elsevier Ltd. All rights reserved.