Now, a team of researchers at the U.S. Food and Drug Administration’s Division of Imaging, Diagnostics, and Software Reliability has developed a new tool designed to catch these flaws before they can do damage. The system, called HistoART, is described in a study published in the journal Neural Computing and Applications. Led by Seyed Kahaki, with contributions from Alexander Webber, Ghada Zamzmi, Adarsh Subbaswamy, Rucha Deshpande, and Aldo Badano, the work compares three fundamentally different strategies for detecting artifacts in whole slide images, and finds that a large foundation model originally trained for general pathology tasks outperforms both a conventional deep learning network and a classical, hand-engineered approach.

The stakes are higher than they might first appear. As hospitals adopt digital pathology workflows, the reliability of downstream image analysis tasks, from tumor detection to grading, depends on the quality of the images feeding into them. An algorithm trained to recognize cancer cells may misinterpret a tissue fold as a suspicious structure, or an air bubble as a region of lost tissue. Previous studies have documented how quality control, or the lack of it, directly affects the accuracy of computational pathology systems. Yet many existing quality control tools rely on fixed rules and handcrafted measurements that may not generalize across the wide variety of scanners, staining protocols, and tissue types encountered in real-world laboratories.

HistoART tackles the problem with a three-way comparison. The first approach, the foundation model-based approach, fine-tunes UNI, a general-purpose foundation model for computational pathology that has been trained on massive collections of pathology images. Foundation models of this kind learn rich, general-purpose visual representations that can be adapted to many downstream tasks with relatively little additional training. The second approach, the deep learning approach, is built on a ResNet50 backbone, a widely used convolutional neural network architecture originally developed for general image recognition. The third, the knowledge-based approach, dispenses with learned representations altogether and instead relies on handcrafted features derived from texture, color, and frequency-based metrics, drawing on decades of classical image analysis research.

All three approaches were trained and evaluated to detect six of the most prevalent artifact types in whole slide images: tissue folds, out-of-focus regions, air bubbles, tissue damage, marker traces, and blood contamination. Each of these artifacts arises at a different stage of the workflow. Tissue folds occur when sections of tissue wrinkle during mounting on the slide. Out-of-focus regions result from imperfect autofocus during scanning. Air bubbles become trapped under the coverslip, marker traces are left by pens used to label slides, and blood contamination can obscure underlying tissue architecture. Detecting such a heterogeneous set of defects demands a system capable of recognizing very different visual patterns, from the sharp linear creases of a fold to the diffuse haze of an out-of-focus patch.

The evaluation was deliberately broad. The researchers assembled a dataset of more than 50,000 image patches sourced from diverse whole slide imaging scanners, including instruments from Hamamatsu, Philips, and Leica Aperio AT2, and drawn from multiple imaging sites. This diversity matters because artifacts can look different depending on the scanner that produced the image, and a detection system that works well on one manufacturer’s output may fail on another’s. By testing across scanners and sites, the team aimed to assess how well each approach would generalize beyond a single laboratory environment, a persistent weakness of many published machine learning studies in pathology.

The results were striking. The foundation model-based approach achieved a patch-wise area under the receiver operating characteristic curve, or AUROC, of 0.995, with a 95 percent confidence interval of 0.994 to 0.995, a level of performance that approaches the theoretical ceiling of a perfect classifier. The ResNet50-based deep learning approach reached an AUROC of 0.977, with a confidence interval of 0.977 to 0.978, while the knowledge-based approach, built on handcrafted features, trailed at 0.940, with a confidence interval of 0.933 to 0.946. The AUROC metric measures a classifier’s ability to distinguish between positive and negative cases across all possible decision thresholds, so the gap between 0.995 and 0.940 represents a substantial difference in real-world reliability, particularly for the subtle or ambiguous artifacts that matter most in clinical practice.

Why would a foundation model so decisively outperform a purpose-built convolutional network? The likely answer lies in the breadth of its pretraining. UNI was trained on enormous and varied collections of pathology imagery, meaning it has already encountered an extraordinary range of tissue appearances, staining variations, and imaging conditions before any artifact-specific fine-tuning begins. When subsequently adapted to the artifact detection task, it brings that prior knowledge to bear, allowing it to distinguish, say, a genuine tissue fold from a naturally occurring tissue edge with greater confidence than a network learning visual features from scratch. The handcrafted approach, meanwhile, is limited by the imagination of its designers: texture descriptors, color statistics, and frequency-domain measures capture certain artifact signatures well but may miss patterns that do not fit predefined mathematical descriptions.

Detection alone, however, is not enough. A laboratory needs to know what to do with the information. To bridge the gap between detection and actionable conclusions, the team developed a quality report scorecard that quantifies the number of high-quality image patches in a dataset and visualizes the distribution of artifact subgroups. This reporting layer transforms raw per-patch predictions into a summary that laboratory staff and algorithm developers can act upon: a slide riddled with folds can be rescanned or excluded, a scanner producing systematic out-of-focus regions can be serviced, and a dataset destined for training a diagnostic model can be filtered to remove compromised patches. In this way, HistoART functions not merely as a classifier but as a quality assurance pipeline for the entire digital pathology workflow.

The researchers describe the resulting system as a multi-branch pipeline that may enhance the reliability of whole slide image analysis by providing a scalable and interpretable approach for improving digital pathology workflows. Interpretability is a key selling point of the scorecard design, since regulators and clinicians are often wary of black-box systems that flag images without explaining why. By visualizing which artifact types dominate a slide or dataset, the tool offers a transparent window into the failure modes of the imaging process. Consistent with the team’s commitment to open science, all the data and methods from the work are available for download on GitHub, allowing other laboratories to adopt, scrutinize, and extend the system. As whole slide imaging continues its march toward becoming the default medium of diagnostic pathology, tools like HistoART suggest that the quality of the digital slide, long treated as an afterthought, is finally being engineered with the same rigor as the diagnoses that depend on it.

Subject of Research: Artifact detection in whole slide histopathology images using foundation models, deep learning, and handcrafted features

Article Title: HistoART: Histopathology artifact detection based on large foundation model

Article References: Kahaki, S., Webber, A., Zamzmi, G., Subbaswamy, A., Deshpande, R., & Badano, A. (2026). HistoART: Histopathology artifact detection based on large foundation model. Neural Computing and Applications, 38(18), Article 753. https://doi.org/10.1007/s00521-026-12454-9

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12454-9

Keywords: histopathology, whole slide imaging, artifact detection, foundation model, UNI, ResNet50, digital pathology, quality control, machine learning, AUROC, cancer diagnostics, image analysis