# TRD 2 - Deep Learning

> **NIH NIH P41** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $289,852

## Abstract

The goals of the Deep Learning TRD of the Advanced Technologies for the National Center for
Image-Guided Therapy (AT-NCIGT) are to investigate revolutionary advances in deep learning (DL) in
the context of image-guided therapy (IGT) of brain, prostate, and lung cancer, and to develop tools that
can be used by the broader IGT research community. The general theme of our research addresses
difficulties associated with creation of training data, which is a significant impediment to the application of
DL to medical images. While DL has had many successes in image-based classification or segmentation
tasks, the methods used are fully supervised, i.e., very large amounts of accurately annotated training
data are needed to achieve best performance. Currently, expert annotation is expensive and laborious in
the case of medical images because accurate segmentation of 3D structures requires manual or
semi-automatic labeling of thousands of voxels per image. Concurrently, large unlabeled or
weakly-labeled data sets are becoming available. For example, the PACS system of a large hospital
might contain tens of millions of scans, but determining accurate disease labels is difficult. There are
currently two promising DL approaches that can be used to address this problem, weakly-supervised
learning (where some labels are absent or otherwise imperfect) and transfer learning (which leverages
labeled data sets that are in some ways similar). The current situation is further exacerbated by a lack of
machine readable metadata, and of methods and tools to support curation of the imaging (e.g., Magnetic
Resonance Imaging (MRI)) and clinical data, alongside annotations and analysis results within a single
data model. The latter leads to fragmentation of data, and non-standard and heterogeneous metadata.
We address these problems by 1) Developing new information theoretic technology for
weakly-supervised deep learning, 2) developing novel training strategies for deep learning for cancer
characterization for transperineal in-bore MRI-guided prostate biopsy, and 3) developing an infrastructure
for curating imaging data for deep learning. The results of this TRD will be DL algorithms, the resulting
models, and tools for annotation and organizing machine-readable metadata that are designed to enable
IGT cancer research for the prostate, the lung, and the brain applications.

## Key facts

- **NIH application ID:** 10326348
- **Project number:** 5P41EB028741-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** William M. Wells
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $289,852
- **Award type:** 5
- **Project period:** 2021-01-06 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10326348

## Citation

> US National Institutes of Health, RePORTER application 10326348, TRD 2 - Deep Learning (5P41EB028741-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10326348. Licensed CC0.

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