# Interpretable Deep Learning Algorithms for Pathology Image Analysis

> **NIH NIH R35** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $447,500

## Abstract

Interpretable Deep Learning Algorithms for Pathology Image Analysis
Abstract
The microscopic examination of stained tissue is a fundamental component of biomedical research and for the
understanding of biological processes of disease which leads to improved diagnosis, prognosis and therapeutic
response prediction. Ranging from cancer diagnosis to heart rejection and forensics the subjective interpretation
of histopathology sections forms the basis of clinical decision making and research outcomes. However, it has
been shown that such subjective interpretation of pathology slides suffers from large interobserver and
intraobserver variability. Recent advances in computer vision and deep learning has enabled the objective and
automated analysis of images. These methods have been applied with success to histology images which have
demonstrated potential for development of objective image interpretation paradigms. However, significant
algorithmic challenges remain to be addressed before such objective analysis of histology images can be used
by clinicians and researchers. Leveraging extensive experience in developing and decimating research software
based on deep learning the PI will pioneer novel algorithmic approaches to address these challenges including
but not limited to: (1) training data-efficient and interpretable deep learning models with gigapixel size microscopy
images for classification and segmentation using weakly supervised labels (2) fundamental redesign of data
fusion paradigms for integrating information from microscopy images and molecular profiles (from multi-omics
data) for improved diagnostic and prognostic determinations (3) developing visualization and interpretation
software for researchers and clinical workflows to improve clinical and research validation and reproducability.
The system will be designed in a modular, user-friendly manner and will be open-source, available through
GitHub as universal plug-and-play modules ready to be adapted to various clinical and research applications.
We will also develop a web resource with pretrained models for various organs, disease states and subtypes
these will be accompanied with detailed manuals so researchers can apply deep learning to their specific
research problems. Overall, the laboratory’s research will yield high impact discoveries from pathology image
analysis, and its software will enable many other NIH funded laboratories to do the same, across various
biomedical disciplines.

## Key facts

- **NIH application ID:** 10029418
- **Project number:** 1R35GM138216-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Faisal Mahmood
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $447,500
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10029418

## Citation

> US National Institutes of Health, RePORTER application 10029418, Interpretable Deep Learning Algorithms for Pathology Image Analysis (1R35GM138216-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10029418. Licensed CC0.

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