# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
### Related Research in Breast Cancer Diagnosis Using Convolutional Neural Networks (CNNs)  
Regarding the medical image domain which deals with breast cancer diagnosis, previous studies examined the inclusion of machine learning approaches into breast cancer computer-aided diagnosis and image classification [29; 37; 38; 39; 40; 41]. The state of the art on machine learning techniques for breast cancer computer aided diagnosis offers a wide range of analysis regarding the current status of CAD systems, when image modalities used and machine learning-based classifiers are taken into consideration [38; 39; 42; 43]. Recent review studies in [44; 45; 46; 47] presented a thorough survey on both traditional ML and DL literature with particular application in breast cancer diagnosis. Also, the bibliographic review in study [47] provided insightful characteristics of some well-known DL networks in breast cancer diagnosis, both in primary and metastatic breast cancer. Investigating the area of computer-aided biomedical images applied in breast cancer diagnosis, there are some recent research studies which show the advantageous features of deep convolutional nets [48; 49; 50]. In particular, in [51] a CNN was applied for breast cancer histopathology images classification, in two different classes, benign and malignant. [PERSON] et al. (2016) implemented CNNs for mass detection in mammographic images in computer-aided diagnosis [52]. [PERSON] et al. (2016) applied CNNs for the classification of breast cancer from histopathological images [53], whereas [PERSON] and [PERSON], (2016) combined deep convolutional networks and SVMs for mass detection on digital mammograms [54]. [PERSON] et al. investigated the application of deep learning and non-negative matrix factorization for breast cancer recognition in mammograms [55]. In the same year, [PERSON] and his colleagues explored the capabilities of unsupervised deep learning to breast density segmentation and mammographic risk scoring [56]. Dealing with breast cancer image detection, CAD systems are fundamentally used in screening mammography for detection of mass [41; 48; 57; 58]. In addition, CNNs have mostly been applied in mammography images and computed tomography (CT) images for classification and segmentation case studies [38; 39; 46; 47; 48; 49]. Furthermore, deep CNNs and transfer-learning models have been applied for the prediction of lymph node metastasis in patients with primary breast cancer on the basis of US images [59], as well as on the basis of histopathological images [60]. It is evident that much research has already been done for the detection and diagnosis of primary breast cancer in the past few years. Some representative CNN applications for breast cancer diagnosis are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of BC** & **Application Type** & **Modality** & **Reference** & **Data Available** \\ \hline Primary & classification & Histopathological & [PERSON] et al. [51] & NO \\ Primary & detection & Mammograms & [PERSON] et al. [55] & YES \\ Primary & classification & Histopathological & [PERSON] et al. [53] & NO \\ Primary & mass detection & Mammograms & [PERSON] et al. [54] & NO \\ Primary & mass detection & Mammograms & [PERSON] et al. [52] & NO \\ Primary & segmentation & Mammograms & [PERSON] et al. [56] & NO \\ Primary & mass detection & Mammograms & [PERSON] et al. [58] & NO \\ Primary & classification & Mammograms & [PERSON] et al. [41] & NO \\ Primary & classification & Mammograms & [PERSON] et al. [57] & NO \\ Metastatic in & prediction & Ultrasound images & [PERSON] et al. [59] & NO \\ lymph nodes & detection and & Histopathological & [PERSON] et al. [60] & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of representative convolutional neural networks (CNNs) for breast cancer (BC) diagnosis. However, our work is devoted to the investigation of deep-learning algorithms on bone metastasis diagnosis in breast cancer patients using bone scintigraphy.