# SCH: Counterfactual Explanations for AI-Assisted Cancer Diagnosis and Subtypiing

> **NIH NIH R01** · RUTGERS, THE STATE UNIV OF N.J. · 2024 · $266,614

## Abstract

Accurate diagnosis of cancer hinges on histopathological assessment, with treatment pivoting upon the
tumor's morphological classification. AI models, especially deep learning (DL) models, have shown great
promise in accurately classifying tumors from histopathological images (often with additional genomic
information). Unlike typical medical images featuring small critical areas, histopathological images are
textural, with relevant texture spanning the entire image, making it challenging to explain DL prediction by
locating areas of an image. Such lack of explainability (and interpretability) severely limits DL's potential as a
valuable tool to pathologists. Thus, there is a critical need to systematically explain DL histopathological
models, beyond mere localization, to substantially improve AI-assisted cancer diagnosis for pathologists.
 This project aims to develop a principled framework to systematically explain DL histopathological
models using counterfactual explanations. The rationale is that while texture features are not amenable to
localization-based explanation methods, one can explain the model by asking counterfactual questions such
as “what histopathological image could have shifted the model prediction from non-aggressive tumor to
aggressive tumor”. Specifically, this project centers around four tasks: (1) Dataset-Level Generative
Explanation: Developing a dataset-level “generative explainer” framework to explain any given DL
histopathological model by generating a spectrum of histopathological images that can lead to an associated
spectrum of different predictions (e.g., from “non-aggressive” through “aggressive” to “highly aggressive”) of
the explained DL model. (2) Instance-Level Counterfactual Explanation: Developing a principled
instance-level “counterfactual explainer” framework to generate instance-specific counterfactual
explanations for a specific histopathological image. (3) Fast Counterfactual Explanation: Developing a
“fast counterfactual explainer” framework to enable real-time generation of counterfactual histopathological
images. (4) From Explanation to Subtype Discovery: Developing a “subtyping counterfactual explainer”
framework that goes beyond explanation to discover novel cancer subtypes (or phenotypes).
RELEVANCE (See instructions):
Accurate diagnosis of cancer hinges on histopathological assessment, with treatment pivoting upon the
tumor's morphological classification. AI models can often accurately classify tumors from histopathological
images, but their lack of interpretability severely hinders their deployment in clinical scenarios. This project
develops a principled framework to systematically explain AI histopathological models using counterfactual
explanations, thereby substantially improving AI-assisted cancer diagnosis for pathologists.

## Key facts

- **NIH application ID:** 11060758
- **Project number:** 1R01CA297832-01
- **Recipient organization:** RUTGERS, THE STATE UNIV OF N.J.
- **Principal Investigator:** Hao Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $266,614
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11060758

## Citation

> US National Institutes of Health, RePORTER application 11060758, SCH: Counterfactual Explanations for AI-Assisted Cancer Diagnosis and Subtypiing (1R01CA297832-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11060758. Licensed CC0.

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