Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
- URL: http://arxiv.org/abs/2603.12016v1
- Date: Thu, 12 Mar 2026 14:58:07 GMT
- Title: Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
- Abstract summary: We develop a novel feature extraction library called Nyxus.<n>Nyxus is designed from the ground up for scalable out-of-core feature extraction for 2D and 3D image data.<n>The comprehensive feature set of Nyxus covers multiple biomedical domains including radiomics and cellular analysis.
- Score: 1.8095863955913634
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Modern imaging instruments can produce terabytes to petabytes of data for a single experiment. The biggest barrier to processing big image datasets has been computational, where image analysis algorithms often lack the efficiency needed to process such large datasets or make tradeoffs in robustness and accuracy. Deep learning algorithms have vastly improved the accuracy of the first step in an analysis workflow (region segmentation), but the expansion of domain specific feature extraction libraries across scientific disciplines has made it difficult to compare the performance and accuracy of extracted features. To address these needs, we developed a novel feature extraction library called Nyxus. Nyxus is designed from the ground up for scalable out-of-core feature extraction for 2D and 3D image data and rigorously tested against established standards. The comprehensive feature set of Nyxus covers multiple biomedical domains including radiomics and cellular analysis, and is designed for computational scalability across CPUs and GPUs. Nyxus has been packaged to be accessible to users of various skill sets and needs: as a Python package for code developers, a command line tool, as a Napari plugin for low to no-code users or users that want to visualize results, and as an Open Container Initiative (OCI) compliant container that can be used in cloud or super-computing workflows aimed at processing large data sets. Further, Nyxus enables a new methodological approach to feature extraction allowing for programmatic tuning of many features sets for optimal computational efficiency or coverage for use in novel machine learning and deep learning applications.
Related papers
- Advancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation [0.1499944454332829]
This work introduces new capabilities to Annotat3D through Harpia.<n>The library is designed to support scalable, interactive segmentation for large 3D datasets in high-performance computing.<n>The system's interactive, human-in-the-loop interface, combined with efficient GPU resource management, makes it particularly suitable for collaborative scientific imaging.
arXiv Detail & Related papers (2025-11-14T21:45:02Z) - fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence [55.582429009401956]
fVDB is a novel framework for deep learning on large-scale 3D data.<n>Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines.
arXiv Detail & Related papers (2024-07-01T20:20:33Z) - LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation [2.0901574458380403]
We propose a new lightweight but efficient model, namely LiteNeXt, for medical image segmentation.<n>The model is trained from scratch with small amount of parameters (0.71M) and Giga Floating Point Operations Per Second (0.42).<n>Experiments on public datasets including Data Science Bowls, GlaS, ISIC2018, PH2, Sunnybrook, and Lung X-ray data show promising results.
arXiv Detail & Related papers (2024-04-04T01:59:19Z) - Dataset Quantization [72.61936019738076]
We present dataset quantization (DQ), a new framework to compress large-scale datasets into small subsets.
DQ is the first method that can successfully distill large-scale datasets such as ImageNet-1k with a state-of-the-art compression ratio.
arXiv Detail & Related papers (2023-08-21T07:24:29Z) - Slideflow: Deep Learning for Digital Histopathology with Real-Time
Whole-Slide Visualization [49.62449457005743]
We develop a flexible deep learning library for histopathology called Slideflow.
It supports a broad array of deep learning methods for digital pathology.
It includes a fast whole-slide interface for deploying trained models.
arXiv Detail & Related papers (2023-04-09T02:49:36Z) - Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure
Learning Algorithms [1.7188280334580197]
Probabilistic graphical models are one common approach to modelling the data generating mechanism.
We present a novel Snakemake workflow called Benchpress for producing scalable, reproducible, and platform-independent benchmarks.
We demonstrate the applicability of this workflow for learning Bayesian networks in five typical data scenarios.
arXiv Detail & Related papers (2021-07-08T14:19:28Z) - Quantized Neural Networks via {-1, +1} Encoding Decomposition and
Acceleration [83.84684675841167]
We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks.
We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
arXiv Detail & Related papers (2021-06-18T03:11:15Z) - Random Features for the Neural Tangent Kernel [57.132634274795066]
We propose an efficient feature map construction of the Neural Tangent Kernel (NTK) of fully-connected ReLU network.
We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice.
arXiv Detail & Related papers (2021-04-03T09:08:12Z) - A Framework for Fast Scalable BNN Inference using Googlenet and Transfer
Learning [0.0]
This thesis aims to achieve high accuracy in object detection with good real-time performance.
The binarized neural network has shown high performance in various vision tasks such as image classification, object detection, and semantic segmentation.
Results show that the accuracy of objects detected by the transfer learning method is more when compared to the existing methods.
arXiv Detail & Related papers (2021-01-04T06:16:52Z) - Captum: A unified and generic model interpretability library for PyTorch [49.72749684393332]
We introduce a novel, unified, open-source model interpretability library for PyTorch.
The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms.
It can be used for both classification and non-classification models.
arXiv Detail & Related papers (2020-09-16T18:57:57Z) - PHOTONAI -- A Python API for Rapid Machine Learning Model Development [2.414341608751139]
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development.
It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences.
arXiv Detail & Related papers (2020-02-13T10:33:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.