Mini-DDSM: Mammography-based Automatic Age Estimation
This work addresses the lack of age estimation methods for mammograms, which could help fill missing age data in medical applications, but it is incremental as it applies existing techniques to a new data type.
The authors tackled the problem of automatic age estimation from mammogram images, a previously unexplored area, by creating a new dataset (Mini-DDSM) and using deep learning features with a Random Forests regressor, achieving an average error of around 8 years in tests.
Age estimation has attracted attention for its various medical applications. There are many studies on human age estimation from biomedical images. However, there is no research done on mammograms for age estimation, as far as we know. The purpose of this study is to devise an AI-based model for estimating age from mammogram images. Due to lack of public mammography data sets that have the age attribute, we resort to using a web crawler to download thumbnail mammographic images and their age fields from the public data set; the Digital Database for Screening Mammography. The original images in this data set unfortunately can only be retrieved by a software which is broken. Subsequently, we extracted deep learning features from the collected data set, by which we built a model using Random Forests regressor to estimate the age automatically. The performance assessment was measured using the mean absolute error values. The average error value out of 10 tests on random selection of samples was around 8 years. In this paper, we show the merits of this approach to fill up missing age values. We ran logistic and linear regression models on another independent data set to further validate the advantage of our proposed work. This paper also introduces the free-access Mini-DDSM data set.