This repository contains a framework for benchmarking failure detection methods under distribution shifts in image classification featuring implementations of baseline methods, data sets and metrics. Additionally to a wide range of built-in methods, this framework can be used to evaluate and compare new methods, models and datasets.

Python package for colocalization of RS-FISH results with either RS-FISH results (e.g from two image channels) 2D/3D, or with an image (only 2D for now).

This collection of solutions brings together existing software solutions to recurring challenges along the imaging pipeline. Currently, it focuses mainly on visualization tasks, but will dynamically be extended by the Helmholtz Imaging Support & Engineering Unit at MDC Berlin.

Project MONAI is a set of open-source, freely available collaborative frameworks built for accelerating research and clinical collaboration in Medical Imaging. The goal is to accelerate the pace of innovation and clinical translation by building a robust software framework that benefits nearly every level of medical imaging, deep learning research, …

microbeSEG is a deep learning-based tool for instance segmentation of roundish objects in 2D and 2D+t data, e.g., microbes. The microbeSEG workflow covers training data creation (with pre-labeling) or import, model training, model evaluation, and the final segmentation of the data. OMERO is used for a comprehensive data management. For …

MONAI Label is an intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets. By utilizing user interactions, MONAI Label trains an AI model for a specific task and continuously learns and updates that model as it receives additional annotated …

Efficient Annotation of Plankton images by Clustering

This repository contains a framework which allows for enhanced model interpretability and model robustness. The framework utilizes interpretable disentangled representations for downstream-task prediction which are in a second step visualized and evaluated through a multipath-attribution mapping.

Segment anything with our Napari integration of Meta AI's new Segment Anything Model (SAM)!

SAM is the new segmentation system from Meta AI capable of one-click segmentation of any object, and now, our plugin neatly integrates this into Napari.

We have already extended SAM's click-based foreground separation to full click-based …

Simultaneous localisation and categorisation of objects, also referred to as object detection, is of high relevance for many applications which rely on rating of objects rather than e.g. pixels or entire images. For this task, the cumbersome and iterative process of method configuration constitutes a major research and application bottleneck. …

When confronted with a new dataset, the application of AI-based segmentation methods is non-trivial. Substantial manual effort is required to optimize the network topology, preprocessing, training procedure and inference strategy to the task at hand. Such adaptations are non-trivial and require substantial expertise to ensure optimal performance on each dataset. …

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms

PickYOLO is a super-fast, universal particle detector, based on the deep-learning real-time object recognition system YOLO (You Only Look Once). After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles …

preCICE is an open-source coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.

Partitioned means that preCICE couples existing programs/solvers capable of simulating a subpart of the complete physics involved in a simulation. This allows for the high flexibility that is …

Standard neural networks for imaging tasks are tied to a certain fixed image resolution they were trained on. Neural operators were proposed in Kovachki, Nikola, et al. “Neural operator: Learning maps between function spaces with applications to PDEs” as a way to generalize neural networks to an infinite-dimensional setting. A …