PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies

• URL: http://arxiv.org/abs/2405.15567v1
• Date: Fri, 24 May 2024 13:55:42 GMT
• Title: PyCellMech: A shape-based feature extraction pipeline for use in medical and biological studies
• Authors: Janan Arslan, Henri Chhoa, Ines Khemir, Romain Valabregue, Kurt K. Benke,
• Abstract summary: Medical researchers obtain knowledge about the prevention and treatment of disability using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. The PyCellMech package extracts three classes of shape features, which are classified as one-dimensional, geometric, and polygonal.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Summary: Medical researchers obtain knowledge about the prevention and treatment of disability and disease using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. In this study, we aim to augment current works by adding to the current mix of shape-based features. The significance of shape-based features has been explored extensively in research for several decades, but there is no single package available in which all shape-related features can be extracted easily by the researcher. PyCellMech has been crafted to address this gap. The PyCellMech package extracts three classes of shape features, which are classified as one-dimensional, geometric, and polygonal. Future iterations will be expanded to include other feature classes, such as scale-space. Availability and implementation: PyCellMech is freely available at https://github.com/icm-dac/pycellmech.

Related papers

• cp_measure: API-first feature extraction for image-based profiling workflows [36.86093674833253]
  cp_measure is a Python library that extracts CellProfiler's core measurement capabilities into a modular API-first tool for programmatic feature extraction.<n>We demonstrate that cp_measure features retain high fidelity with CellProfiler features while enabling seamless integration with the scientific Python ecosystem.
  arXiv  Detail & Related papers  (2025-07-01T19:51:32Z)
• PyPulse: A Python Library for Biosignal Imputation [58.35269251730328]
  We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers. We released PyPulse under the MIT License on Github and PyPI.
  arXiv  Detail & Related papers  (2024-12-09T11:00:55Z)
• GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules [16.98169256565552]
  We set up a data collection effort for 200K pairs of ground-state geometric structures and biomedical texts. We propose the GeomCLIP framework to enhance for multi-modal representation learning from molecular structures and biomedical text.
  arXiv  Detail & Related papers  (2024-11-16T15:15:24Z)
• Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images [0.0]
  We introduce Grad-CAMO, a novel single-cell interpretability score for supervised feature extractors. Grad-CAMO measures the proportion of a model's attention that is concentrated on the cell of interest versus the background.
  arXiv  Detail & Related papers  (2024-03-26T11:48:37Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision [119.29105800342779]
  MedShapeNet was created to facilitate the translation of data-driven vision algorithms to medical applications. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks.
  arXiv  Detail & Related papers  (2023-08-30T16:52:20Z)
• Deep Learning Enabled Time-Lapse 3D Cell Analysis [7.094247258573337]
  This paper presents a method for time-lapse 3D cell analysis. We consider the problem of accurately localizing and quantitatively analyzing sub-cellular features. The code is available on Github and the method is available as a service through the BisQue portal.
  arXiv  Detail & Related papers  (2022-08-17T00:07:25Z)
• PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation [59.70430570779819]
  We introduce a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. Our key insight is to copy and deform patches from the partial input to complete missing regions. We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps.
  arXiv  Detail & Related papers  (2022-07-24T18:59:09Z)
• SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification [4.992154875028543]
  Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. SubOmiEmbed produces comparable results to the baseline OmiEmbed with a much smaller network and by using just a subset of the data. This work can be improved to integrate mutation-based genomic data as well.
  arXiv  Detail & Related papers  (2022-02-03T16:39:09Z)
• PyHealth: A Python Library for Health Predictive Models [53.848478115284195]
  PyHealth is an open-source Python toolbox for developing various predictive models on healthcare data. The data preprocessing module enables the transformation of complex healthcare datasets into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches.
  arXiv  Detail & Related papers  (2021-01-11T22:02:08Z)
• ATOM3D: Tasks On Molecules in Three Dimensions [91.72138447636769]
  Deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance.
  arXiv  Detail & Related papers  (2020-12-07T20:18:23Z)
• 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)

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.