Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification

SIMPLE SUMMARY: In this study, we propose a novel deep-learning method based on multi-stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models to classify mammographic masses as either benign or malignant. The proposed method alleviates the challenge of obtaining large amounts of labeled mammogram training data by utilizing a large number of cancer cell line microscopic images as an intermediate domain of learning between the natural domain (ImageNet) and medical domain (mammography). Moreover, our method does not utilize patch separation (to segment the region of interest before classification), which renders it computationally simple and fast compared to previous studies. The findings of this study are of crucial importance in the early diagnosis of breast cancer in young women with dense breasts because mammography does not provide reliable diagnosis in such cases. ABSTRACT: Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer.