Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images

Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91% mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.