# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 1 Introduction  
Machine learning techniques with a dedicated emphasis on deep learning methodologies have been applied successfully on the field of health informatics as an assistive tool for the relief of workload that specialized medical personnel need to carry [1, 2] and for educational purposes [3]. The iterative process of continuously evolving the concerned algorithms has brought to light more effective implementations that exceed the human eye discriminative capability [4, 5, 6] and enhance the objectivity criteria by means of visual patterns' quantification. These improved implementations are, therefore, applied for the reliable and precise prognosis and diagnosis of pathologic cases. The processing of traditional medical imaging material such as MRI's, X-ray's, Ultrasound, Endoscopy, Thermography, Tomography, Microscopy, and Dermoscopy has been transformed to each digital version providing numerous benefits in a variety of tasks that were earlier performed manually [7, 8, 9, 10, 11, 12, 13]. The abovementioned tasks fall under the umbrella of well-known computer vision tasks, namely, semantic segmentation [14, 15], generation [16], registration [17, 18], image classification [15], and object detection [19]. In the last decade, the registered and documented ability of deep convolutional networks to identify visual patterns beyond the human perspective is gaining popularity in the field of digital pathology as well. Driven by the rise of digital scanners that produce whole slide images, the assessment of human tissue in histopathology images can be conducted by means of a virtual microscope. A whole slide image, containing in average 10 GB, can satisfy the needs of data hungry deep convolutional networks and alleviate issues concerning the creation, handling, and preservation of glass slides. In this framework, patches,extracted from whole slide images, are inserted as inputs in deep convolution networks in a supervised or unsupervised manner, exploiting the benefits of latest developments in the field of deep learning such as transfer learning with pretrained models and the unlabeled training via autoencoders or Generative Adversarial Networks (GANs) [20; 21]. Apart from deep learning techniques, machine learning algorithms have been utilized in the field of digital pathology for content-based image retrieval and classification of histopathology images. While firstly introduced for text classification, the Bag of Words technique is utilized in [22; 23] for the description of dense imagery content and its exploitation on the designated tasks. However, whole slide imaging is introduced to the scientific community with a newly breed set of challenges that needs to be addressed, mainly related to the polymorphism of the data formats, the big data management, the standardization of staining and the transparency, and explainability of predictions. In this work, we focus on breast and colon cancer, which are distinguished as two of the most lethal cases, among different kinds of cancer that cause high rates of mortality worldwide. Breast cancer is the first leading disease in terms of incidents for women [24], whereas colon cancer is classified as second for women and third for men [25]. Utilizing automated machine learning techniques for the prognosis and diagnosis is vital for the early detection of malignancies in both cases aiming at total healing and avoidance of metastasis [26; 27]. Towards this direction, researchers in the field of digital pathology have been occupied with the specific forms of cancer systematically. Although the availability of datasets is immense and reported results of the deep learning techniques are high [28], the need for explaining the connection between the input and the result is overlooked, yet compelling especially in the case of predictive models in healthcare information systems where the responsibility for high-stake decisions is heavy.