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Towards Multiple Enhancement Styles Generation in Mammography

Mammography is a well-established imaging modality for early detection and diagnosis of breast cancer. The raw detector-obtained mammograms are difficult for radiologists to diagnose due to the similarity between normal tissues and potential lesions in the attenuation level and thus mammogram enhancement (ME) is significantly necessary. However, the enhanced mammograms obtained with different mammography devices can be diverse in visualization due to different enhancement algorithms adopted in these mammography devices. Different styles of enhanced mammograms can provide different information of breast tissue and lesion, which might help radiologists to screen breast cancer better. In this paper, we present a deep learning (DL) framework to achieve multiple enhancement styles generation for mammogram enhancement. The presented DL framework is denoted as DL-ME for simplicity. Specifically, the presented DL-ME is implemented with a multi-scale cascaded residual convolutional neural network (MSC-ResNet), in which the output in the coarser scale is used as a part of inputs in the finer scale to achieve optimal ME performance. In addition, a switch map is input into the DL-ME model to control the enhancement style of the outputs. To reveal the multiple enhancement styles generation ability of DL-ME for mammograms, clinical mammographic data from mammography devices of three different manufacturers are used in the work. The results show that the quality of the mammograms generated by our framework can reach the level of clinical diagnosis and enhanced mammograms with different styles can provide more information, which can help radiologists to efficiently screen breast cancers.

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