Automated Detection of DRESS Syndrome

by Sirada Kittipaisarnkul
Radboud University Medical Center (RadboudUMC)

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Overview

This project presents a weakly supervised deep learning pipeline for the automated classification of Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) versus Morbilliform Drug Eruption (MDE) from whole slide images (WSIs). It incorporates modern Multiple Instance Learning (MIL) techniques and multi-resolution feature extraction to assist clinical diagnosis in dermatopathology.

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Dataset

Note: This dataset is private and cannot be redistributed.

• 233 WSIs from:
  • Massachusetts General Hospital (MGH)
  • Brigham and Women's Hospital (BWH)
  • Ohio State University Wexner Medical Center (OSU)
• Each WSI is labeled weakly at slide level (DRESS or MDE)

Examples

DRESS WSI	MDE WSI

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Methodology

1. Patch Extraction & Feature Encoding (TRIDENT)

• WSIs are tiled at 10× and 20× magnification.
• TRIDENT extracts patch-level features using multiple encoders:
  • UNI (2024)
  • Gigapath (2024)
  • Hoptim1 (internal encoder)

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2. Multiple Instance Learning (MIL)

We compare three MIL-based pipelines:

A. ABMIL (Attention-based MIL)

Global attention pooling on patch features.

B. CLAM (Clustering-constrained Attention MIL)

Selects top-k most informative patches using attention.
Trained per encoder, then ensembled via late fusion (product of probabilities).

C. Top-k ZoomMIL Refinement

Uses top-k coordinates from 10× CLAM to zoom into 20× regions.
Aggregates both magnifications using average or sum fusion.

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