Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging

IF 0.6 4区 工程技术 Q4 Engineering
Mani Manavalan
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引用次数: 1

Abstract

The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.
在高分辨率和低标记的组织病理成像中诊断表皮基底鳞状细胞癌
从每个患者的临床记录中发现模式的最合适方法是创建一个包,其中包含各种症状形式的示例。医学诊断的目标是首先找到有用的,然后将它们映射到一种或多种疾病上。在某些方面,患者通常被表示为载体。病理学家和皮肤病理学家经常诊断基底细胞癌(BCC),这是人类最常见的皮肤癌之一。通过产生诊断思想,即计算机辅助诊断来改善组织学诊断,是一个备受争议的研究课题,旨在提高安全性、质量和效率。由于其性能的提高,机器学习方法正在迅速得到应用。另一方面,通过扫描组织学切片获得的典型图像通常具有当今最先进的神经网络无法满足的分辨率。此外,弱标签阻碍了网络训练,因为只有一小部分图像标记了疾病类别,而大多数图像与非疾病类别惊人地相似。这项工作的目的是看看基于注意力的深度学习模型是否可以在组织学切片中检测基底细胞癌,并克服全幻灯片图像的超高分辨率和差标记。AUC为0.99,表明基于注意力的模型可以实现近乎完美的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nuclear Engineering International
Nuclear Engineering International 工程技术-核科学技术
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Information not localized
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