# Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis

arXiv: https://arxiv.org/abs/2404.07676 · HTML: https://mlsanity.com/paper/2404.07676/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [Quilt-1M: One Million Image-Text Pairs for Histopathology](https://mlsanity.com/paper/2306.11207/) (2023)
- [Re-identification from histopathology images](https://mlsanity.com/paper/2403.12816/) (2024)

## Built on by (most cited first, 3 of 3)

- [Mask of truth: model sensitivity to unexpected regions of medical images](https://mlsanity.com/paper/2412.04030/) (2024)
- [Dataset Diversity Metrics and Impact on Classification Models](https://mlsanity.com/paper/2603.15276/) (2026)
- [Effortless Vision-Language Model Specialization in Histopathology without Annotation](https://mlsanity.com/paper/2508.07835/) (2025)

## Abstract (arXiv)

The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks.