用超参数调整优化CNN模型增强组织病理图像分类的稳健性

Anil Johny, Dr. Madhusoodanan K. N., Dr. Tom J Nallikuzhy
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引用次数: 3

摘要

病理学领域的发展如此之快,以至于现在可以用数字扫描仪从玻璃载玻片上产生高质量的图像。应用于这种数字化图像的图像分析算法有助于自动诊断任务,同时协助医学专家。在组织病理学图像中成功检测恶性肿瘤很大程度上取决于放射科医生的专业知识,尽管他们有时不同意他们的决定。计算机辅助诊断为诊断提供了第二意见的平台,提高了专家意见的可靠性。与依赖于人工提取特征的传统方法相比,深度学习提供了有希望的结果,这种方法既耗时又费力。由于尺寸巨大,整个幻灯片图像被转换成补丁,并使用卷积神经网络(CNN)进行训练,卷积神经网络是图像深度学习模型的一种变体。实验结果表明,与已有的预训练模型相比,本文提出的局部分类模型的分类准确率为92.8%,ROC曲线下面积为0.97,与预训练模型接近。
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Optimization of CNN Model With Hyper Parameter Tuning for Enhancing Sturdiness in Classification of Histopathological Images
The field of pathology has advanced so rapidly that it is now possible to produce whole slide images (WSI) from glass slides with digital scanners producing high-quality images. Image analysis algorithms applied to such digitized images facilitate automatic diagnostic tasks whilst assisting a medical expert. Successful detection of malignancy in histopathological images largely depends on the expertise of radiologists, though they sometimes disagree with their decisions. Computer-aided diagnosis provides a platform for a second opinion in diagnosis, which can improve the reliability of an expert's opinion. Deep learning provides promising results compared to the conventional approach that relies on manual extraction of features which is time-consuming and labor-intense. Due to the huge size, whole slide images are converted into patches and trained using a Convolutional Neural Network (CNN), a variant of the deep learning model for images. Experimental results show that the proposed native model achieved patch wise classification accuracy of 92.8% and area under ROC curve 0.97 which is close to the values while comparing with the existing pre-trained models.
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