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Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging datasets must be accompanied with…
BioImageLoader (BIL) is a python library that handles bioimage datasets for machine learning applications, easing simple workflows and enabling complex ones. BIL attempts to wrap the numerous and varied bioimages datasets in unified…
Bio image analysis has recently become one keystone of biological research but biologists tend to get lost in a plethora of available software and the way to adjust available tools to their own image analysis problem. We present BIII,…
Data sharing is a key factor for ensuring reproducibility and transparency of scientific experiments, and neuroimaging is no exception. The vast heterogeneity of data formats and imaging modalities utilised in the field makes it a very…
In the rapidly evolving field of bioimaging, the integration and orchestration of Findable, Accessible, Interoperable, and Reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO, a bridge connecting OMERO, a…
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and…
In this paper, we present BIMS (Biomedical Information Management System). BIMS is a software architecture designed to provide a flexible computational framework to manage the information needs of a wide range of biomedical research…
Quantitative analysis of multidimensional biological images is useful for understanding complex cellular phenotypes and accelerating advances in biomedical research. As modern microscopy generates ever-larger 2D and 3D datasets, existing…
Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using…
While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these academic assets into freely accessible tools. This tutorial…
Advances in synthetic biology and nanotechnology have contributed to the design of tools that can be used to control, reuse, modify, and re-engineer cells' structure, as well as enabling engineers to effectively use biological cells as…
Image annotation and large annotated datasets are crucial parts within the Computer Vision and Artificial Intelligence fields.At the same time, it is well-known and acknowledged by the research community that the image annotation process is…
Underwater image enhancement is vital for marine conservation, particularly coral reef monitoring. However, AI-based enhancement models often face dataset bias, high computational costs, and lack of transparency, leading to potential…
Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that…
We present a novel multi-modal bio-sensing platform capable of integrating multiple data streams for use in real-time applications. The system is composed of a central compute module and a companion headset. The compute node collects,…
Data standardization has become one of the leading methods neuroimaging researchers rely on for data sharing and reproducibility. Data standardization promotes a common framework through which researchers can utilize others' data. Yet, as…
The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it…
Researchers in biomedical research, public health, and the life sciences often spend weeks or months discovering, accessing, curating, and integrating data from disparate sources, significantly delaying the onset of actual analysis and…
Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an…
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar…