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

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $290,505

## Abstract

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.

## Key facts

- **NIH application ID:** 10488814
- **Project number:** 5R01EB032896-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Shandong Wu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $290,505
- **Award type:** 5
- **Project period:** 2021-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488814

## Citation

> US National Institutes of Health, RePORTER application 10488814, SCH: Leverage clinical knowledge to augment deep learning analysis of breast images (5R01EB032896-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10488814. Licensed CC0.

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