利用卷積神經網路自動化非侵入式基底細胞癌偵測

Abstract

基底細胞癌是一種最常見的皮膚癌，而傳統上，它必須透過侵入式且耗時的組織學檢測，因此活體內顯影的技術，比如說HGM，被用來當做非侵入式的診斷基礎，但HGM會產生大量的影像導致檢測人員需要花大量的時間去檢測。在這篇論文中，我們最主要集中在如果使用客製化且有效率的CNN模型來去自動偵測BCC的特徵，我們最好的模型可以達到比AlexNet更好的結果，而且只需要AlexNet不到1\%的參數量。而這篇論文的方法也可以套用在其他相似的醫療圖片上。

Diagnosis of basal cell carcinoma (BCC), the most common skin cancer, is made by histologic examination traditionally. Yet the process is invasive and time-consuming. In vivo imaging modalities such as harmonic generation microscopy (HGM) was therefore developed for noninvasive diagnosis of BCC. However the images acquired by HGM are too many for physicians to interpret manually. Thus, in this paper we focus on detecting features of BCC automatically by customizing compact and efficient convolutional neural network (CNN) models on HGM images of BCC. Our best model achieves a better result than AlexNet cite{krizhevsky2012imagenet}, while using less than its 1\% number of parameters. The study indicated the potential solution of using customized CNN to detect the features in similar imaging modalities.