# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 2 Materials and Methods  
### Datasets  
This study utilized six publicly available mammography image datasets from various origins and locations. The utilized datasets in this study are as follows:  
* VinDr-Mammo [12]: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography (FFDM) that consists of 5000 four-view exams with breast-level assessment and finding annotations following the Breast Imaging Report and Data System (BI-RADS). Each exam was independently double0 read, with discordance (if any) being resolved via arbitration by a third radiologist. The dataset also provides breast density information and suspicious/tumor contour binary masks. The dataset was collected from VinDr Hospital in Vietnam.
* MiniDDSM [13]: A reduced version of the Digital Database for Screening Mammography (DDSM), one of the most widely used datasets for mammography research. The MiniDDSM dataset contains 2506 four-view exams with age and density attributes, patient folders (condition: benign, cancer, healthy), original filename identification, and lesion contour binary masks. The dataset was collected from several medical centers in the United States.
* CMMD [14]: The Chinese Mammography Database is a large-scale dataset of FPDM images from Chinese women. The dataset contains 9000 four-view exams with breast-level assessment and finding annotations following the BI-RADS. The dataset also provides age and density information. The dataset was collected from several hospitals in China.
* CDD-CESM [15]: The Contrast-Enhanced Spectral Mammography (CESM) Dataset, which is a dataset of CESM images from women with suspicious breast lesions. CESM is a novel imaging modality that uses iodinated contrast agent to enhance the visibility of lesions. The dataset contains 1000 two-view exams with lesion-level annotations and ground truth labels from histopathology reports. The dataset was collected from several hospitals in Spain.
* BMCD [16]: The Breast Masses Classification Dataset is a dataset of FPDM images from women with benign or malignant breast masses. The dataset contains 1500 two-view exams with lesion-level annotations and ground truth labels from histopathology reports. The dataset was collected from several hospitals in Turkey.
* RSNA [17]: The Radiological Society of North America (RSNA) Dataset, which is a dataset of FPDM images from women with pulmonary embolism (PE). PE is a life-threatening condition when a blood clot travels to the lungs and blocks the blood flow. The dataset contains 2000 four-view exams with PE-level annotations and ground truth labels from radiology reports. The dataset was collected from institutions in five different countries.  
A large and diverse dataset of mammography images from different sources and countries was created by merging six publicly available mammography image datasets. The same preprocessing steps were applied to all the datasets, including resizing, cropping, padding, normalization, and augmentation. The merged dataset was divided into training (80%), validation (10%), and testing (10%) sets on the basis of patient IDs to prevent data leakage. Table 1 presents the summary statistics of the merged dataset. Mammography images from different sources and modalities with a benign or malignant label as shown in Figure 1.