Imran Muhammad, Islam Tiwana Mohsin, Mohsan Mashood Mohammad, Alghamdi Norah Saleh, Akram Muhammad Usman
Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan.
Front Med (Lausanne). 2024 Apr 29;11:1380405. doi: 10.3389/fmed.2024.1380405. eCollection 2024.
Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.
In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods.
The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system.
This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.
非黑色素瘤皮肤癌包括基底细胞癌（BCC）、鳞状细胞癌（SCC）和表皮内癌（IEC），在皮肤癌中发病率最高。智能决策支持系统可以解决主题专家数量有限的问题，并有助于缓解城市中心和偏远地区医疗服务的差距。
在本研究中，我们提出了一种基于Transformer的模型，用于对组织病理学图像进行分割，不仅可以将其分为炎症和癌症（如BCC、SCC和IEC），还可以识别在决策中重要的皮肤组织和边界。这些组织类型的准确分割最终将导致非黑色素瘤皮肤癌的准确检测和分类。在分类之前根据组织类型及其视觉表示进行分割，增强了病理学家和医生对其的信任，因为这与大多数病理学家处理该问题的方式相关。通过不确定性图可视化模型预测的置信度也是本研究与大多数深度学习方法的区别所在。
使用公开可用数据集对所提出的系统进行评估。我们提出的分割系统的应用表现良好，F1分数为0.908，平均交并比（mIoU）为0.653，平均准确率为83.1%，表明该系统可以成功用作决策支持系统，并有可能随后成熟为一个全自动系统。
本研究试图使用应用于组织病理学皮肤图像的基于Transformer的深度学习技术，实现最常见的非黑色素瘤皮肤癌分割的自动化。所提出的系统根据组织类型对组织病理学图像进行高度准确的分割和视觉表示，这意味着该系统可用于与皮肤相关的常规病理学任务，包括癌症和其他异常检测、它们的分类，以及在癌症病例中测量手术切缘。
