Conference Agenda

Brief Description and Outline:

Joint introduction (~12 min)

Ella Bahry and Deborah Schmidt will give a short overview of PixelPatrol, and Hans Werners and Philipp Heuser will introduce the Helmholtz Model Zoo. The two tools address opposite ends of the AI pipeline in scientific imaging: PixelPatrol helps you understand and curate your data before training, while the Helmholtz Model Zoo lets you share and run inference with the resulting trained models.

Track A: PixelPatrol

PixelPatrol is an open-source quality control and data exploration tool for scientific image datasets. It systematically profiles imaging data and generates interactive dashboard reports with statistics and visualizations, making it a useful first step before any computationally intensive analysis or AI model training.

Participants will work with their own datasets or a provided example (aqQua foundation model training data). The session covers generating comprehensive quality reports; interpreting visualizations including file stats, metadata consistency, and image statistics; interactive filtering and grouping; identifying outliers, artifacts, and acquisition inconsistencies; and an introduction to developing custom PixelPatrol packages and plugins.

Track B: Helmholtz Model Zoo

The Helmholtz Model Zoo (HMZ) is a cloud-based platform for sharing and running inference on deep learning models across the Helmholtz Association. It is integrated with Helmholtz infrastructure, including Helmholtz ID, dCache, and DESY's HPC cluster with NVIDIA L40S GPUs. Users can run inference via both a web interface and a REST API, with support for datasets from gigabytes to terabytes. NVIDIA Triton Inference Server and Slurm manage GPU resources, and virtual organizations enable fine-grained access control across scientific domains.

Participants will go through the steps required to prepare and deploy a model on the HMZ, including an overview of available deployment templates. Participants are encouraged to bring their own model or an open-source model they would like to deploy (for example, from Hugging Face); an example model will also be provided. Those who want to prepare in advance are welcome to reach out at support@helmholtz-imaging.de.

Goals:

Pixel Patrol: This hands-on workshop introduces participants to systematic data curation practices for scientific image datasets using PixelPatrol, an open-source tool for dataset exploration and quality control.

Participants will learn to identify data quality issues before computationally expensive analysis, explore dataset characteristics interactively, and implement quality control workflows that prevent common pitfalls in imaging pipelines and AI model training.

With PixelPatrol it will be possible to generate fingerprints of the training data, which can be used in the context of the HMZ to support users to identify trained models where the fingerprint of the training data suggests successful inference to the users data

We will also provide an outlook on how both Pixel Patrol and the Model Zool will be integrated in the near future.

Presenters Experience:

Ella Bahry and Deborah Schmidt (Helmholtz Imaging, MDC Berlin) co-lead PixelPatrol development and both teach at HIDA.

Hans Werners (Helmholtz Imaging, DESY Hamburg) is the primary developer of the Helmholtz Model Zoo.

Philipp Heuser leads the Helmholtz Imaging Support and Engineering team at DESY and is deeply involved in HMZ development.

 

Target Audience:

Imaging researchers working with large or heterogeneous datasets, scientists preparing data for AI/deep learning workflows, data curators, research software engineers, and ML practitioners wanting to make their models accessible to the Helmholtz community. Participants should bring a laptop and, optionally, a dataset or model they would like to work with.

 

Keywords:

data curation, image quality control, dataset exploration, deep learning workflows, data integrity, metadata validation, FAIR research, model deployment, Helmholtz Model Zoo, inference