SCH: Leverage clinical knowledge to augment deep learning analysis of breast images

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Artificial intelligence (AI) technologies have achieved remarkable success in medical image-based applications. Today, there are unprecedented needs in developing novel strategies and methodologies to enable robust, trustworthy, and accessible AI for various applications. Classic deep learning training is driven purely by data. In the medical domain, clinical knowledge is often available and useful, but is mostly ignored in the current practice of AI research. Incorporating clinical knowledge into deep learning modeling requires an in-depth understanding of medical context/workflow. This calls for multi-disciplinary collaborative research using computational techniques and clinical sciences to advance the biomedical data/AI research. The overall goal of this project is to develop a new paradigm of deep learning that combines imaging data and clinical knowledge to augment breast cancer diagnosis, risk assessment, and lesion detection. We will develop technical innovations on breast imaging to address deep learning modeling on small datasets, longitudinal examinations, and content-efficient images, through three specific aims: Aim 1: Formulate auxiliary tasks/assessment into model training of CNNs for breast cancer diagnosis on small datasets; Aim 2: Employ biological relationships of images to guide deep learning structure design for breast cancer risk prediction using longitudinal data; Aim 3: Develop a knowledge-guided unsupervised pipeline for identification of a suspicion map to support deep learning analysis on content-efficient images. These aims represent novel applied methodological development to build roust deep learning models for important clinical imaging applications. We have strong preliminary results for each aim and an experienced research team covering computational, biomedical, engineering, and clinical sciences. Our proposed study has a broader impact on developing robust and innovative AI strategies/methods to enable clinical imaging AI applications. Going beyond breast imaging, our proposed concepts, paradigms, and methods can also be adapted/applicable to other diseases and imaging modalities, leading to benefits for a wide range of biomedical imaging analyses. Any algorithms, knowledge, insights, and experience gained from this study will have a direct and substantial impact on the rapid evolvement and applications of medical imaging AI devices, ultimately benefiting the researchers, clinicians, and patients.