NCI Data Jamboree (Project Abstract Submission): Submission #41

Submission information
Submission Number: 41
Submission ID: 189073
Submission UUID: dc82e8cf-23ac-4b7f-acde-b30f534cd5a3

Created: Mon, 07/27/2026 - 09:52
Completed: Mon, 07/27/2026 - 10:01
Changed: Mon, 07/27/2026 - 10:01

Remote IP address: 10.208.24.192
Submitted by: Anonymous
Language: English

Is draft: No
First Name Xuelu (Jeff)
Middle Initial
Last Name Liu
Degree(s) MS
Position/Title/Career Status Director, Data Management and Strategy
Organization Dana-Farber Cancer Institute
Organization Address Boston
Email J_Liu@dfci.harvard.edu
List of Additional Authors
  • First Name: David
    Last Name: Yang
    Post-nominal letters: MD
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Alexander
    Last Name: Haas
    Post-nominal letters: MD
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Mark
    Last Name: Apinis
    Post-nominal letters: MS
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Yantong
    Last Name: Cui
    Post-nominal letters: MS
    Affiliation: Dana-Farber Cander Institute
  • First Name: Chloe
    Last Name: Flamm
    Affiliation: Dana-Farber Cancer Institute
  • First Name: Ittai
    Last Name: Dayan
    Post-nominal letters: MD
    Affiliation: Rhino Federated Computing
  • First Name: Adrish
    Last Name: Sannyasi
    Affiliation: Rhino Federated Computing
Abstract Category Developing, refining, or validating tools, methods, algorithms, and pipelines
Abstract Keywords Digital Pathology; batch effects; federated learning; biomarker prediction; feature-space harmonization
Abstract Title Mitigating Batch Effects in Digital Pathology via Feature-Space Harmonization of H&E Foundation Model Embeddings in A Federated Learning Scenario
Abstract Scientific/Technical Question
Batch effects caused by variability in staining, tissue processing, slide scanning, and institutional workflow reduce generalizability in digital pathology. We propose a study to evaluate harmonization strategies in a federated learning scenario and determine whether feature-space normalization of H&E foundation model embeddings can mitigate batch effects more effectively than standard stain normalization methods and improve slide-level biomarker prediction across heterogeneous datasets and institutions. We will benchmark conventional stain normalization and data augmentation methods and, as a stretch goal, benchmark feature-space harmonization methods for pathology embeddings.

Why This Matters
This project is relevant to the broader community because it addresses a major barrier to deploying robust AI across cohorts and clinical sites. Expected deliverables include a benchmark of conventional stain normalization versus feature-space denoising, a reproducible pathology AI workflow spanning NCI-accessible and federated institutional data, preliminary evidence on cross-institutional robustness, and a framework for future NCI–academic–industry collaboration in privacy-preserving computational pathology.

Data Types / Datasets
With support from the NCI Office of Data Sharing, we will use datasets available through NCI data commons and ecosystems, including TCGA and possible extensions to the CCDI Data Hub, prioritizing cohorts with H&E whole-slide images, linked molecular biomarker annotations, relevant clinical metadata, and source/site metadata when available. Initial biomarker tasks include NSCLC EGFR, colorectal cancer MSI, and breast cancer BRCA1/2-related status.

Methods / Environment
Whole-slide images will undergo quality control, tissue detection, and tile extraction. Baseline benchmarking will compare no normalization, Macenko, Reinhard, and Vahadane; tile embeddings will be extracted using ResNet50, UNI, UNIv2, and Virchowv2; and slide-level prediction will use ABMIL. Rhino’s Federated Computing platform will enable privacy-preserving external evaluation on DFCI-local pathology data without moving raw data.