Model Interpretability | Matthew Vaishnav Computational Pathology, Federated Oncology Learning, and Mathematical Validation Infrastructure

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Matthew Vaishnav

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Overview

Home

What this project is

Repository structure

Platform overview

Claim status

Research

Literature positioning

Quickstart

Getting started

Reproducing experiments

Running benchmarks

Models

Models overview

TransnnMIL v2.0

Foundation encoders

Model cards

Federated Learning

PathologyFL

FAIR-WEIGHTS-H

Implementation status

Privacy and DP

Secure aggregation

Byzantine robustness

Validation

Validation overview

Synthetic validation

PCam smoke report

PCam benchmark plan

Camelyon17 plan

Results

PCam results

Performance comparison

Engineering

Architecture

Testing status

Benchmark system

Security hardening

Deployment

Roadmap

Roadmap

Limitations

Changelog

Research documentation. Not clinical validation or regulatory clearance.

Computational pathology, federated oncology learning, and mathematical validation infrastructure.