# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 1 Introduction  
For decades, visual examination of hematoxylin-eosin (H&E)-stained tissue slides by pathologists has been the foundation of cancer diagnosis for different types of cancers [1]. However, it is well known that visual assessment of tissue slide is often subjective and thus intra- and inter-observer variabilities are unavoidable [2; 3]. Automated computational methods can help to overcome such limitations and to supplement current pathology workflow by delivering reproducible diagnosis for various cancer types [4]. Furthermore, considering predicted shortage of pathologists in the near future and increased workload for the analysis of various molecular tests, automation on histopathologic diagnosis will be necessary to optimize workload in many pathology laboratories [5; 6].  
Recently, primary diagnosis on digitalized pathologic images was approved in the US and digitization of glass tissue slide into whole slide images (WSIs) has been exploding around the globe [7; 8]. Therefore, massive amounts of digital pathology images are now available for researchers who have been interested in the automation of diagnosis for cancer tissues. Furthermore, the performance of computer-based image analysis has been much improved by the adoption of deep learning technology [9]. In contrast to other classic machine learning approaches, deep learning can learn relevant features for given tasks directly from raw input datasets, and thus eliminate the necessity of domain-specific feature extraction processes [10]. Combined with huge digital WSI datasets, deep learning has been rapidly adopted for the pathologic diagnosis tasks such as Gleason grading ofprostate cancer [4], detection of invasive ductal carcinoma in breast cancer [11], detection of metastasis for breast cancer [12], and detection of tumor tissues in gastric cancer [13].  
The generalizability of a deep learning system is an important issue for a developed system to be more widely applicable. Normal and tumor cells are thought to have some common differences in cell shape and nuclear morphology. Therefore, a deep learning system trained to discriminate normal/tumor tissues in a cancer type can be applied to other cancer types. However, previous studies were focused on tissues from a specific cancer type and the generalizability of deep learning systems on cancer diagnosis tasks is still unclear [14]. Since different cancers originate from different anatomical sites with specific structures, the diagnosis performance of a deep learning system may not necessarily extend beyond trained cancer type [15; 16].  
Therefore, in the present study, we built deep learning-based normal/tumor tissue classifiers for six different cancer types and cross-validated the performance of the classifiers to understand generalizability of deep learning-based cancer diagnosis system. Furthermore, it is well known that frozen and formalin-fixed paraffin-embedded (FFPE) tissues have different morphology because of freezing artifacts [17]. Thus, we also cross-validated the classifiers for the frozen and FFPE tissues of each cancer type. The results of the current study can clearly solve the undetermined question on the generalizability of deep learning-based normal/tumor tissue classifiers.