EXACT: A collaboration toolset for algorithm-aided annotation of images
with annotation version control
- URL: http://arxiv.org/abs/2004.14595v3
- Date: Mon, 19 Jul 2021 12:29:32 GMT
- Title: EXACT: A collaboration toolset for algorithm-aided annotation of images
with annotation version control
- Authors: Christian Marzahl, Marc Aubreville, Christof A. Bertram, Jennifer
Maier, Christian Bergler, Christine Kr\"oger, J\"orn Voigt, Katharina
Breininger, Robert Klopfleisch, and Andreas Maier
- Abstract summary: EXACT enables the collaborative interdisciplinary analysis of images online and offline.
Software can be adapted to diverse applications such as counting mitotic figures with a screening mode.
It has already been successfully applied to a broad range of annotation tasks.
- Score: 7.6457287740201
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In many research areas, scientific progress is accelerated by
multidisciplinary access to image data and their interdisciplinary annotation.
However, keeping track of these annotations to ensure a high-quality
multi-purpose data set is a challenging and labour intensive task. We developed
the open-source online platform EXACT (EXpert Algorithm Collaboration Tool)
that enables the collaborative interdisciplinary analysis of images from
different domains online and offline. EXACT supports multi-gigapixel medical
whole slide images as well as image series with thousands of images. The
software utilises a flexible plugin system that can be adapted to diverse
applications such as counting mitotic figures with a screening mode, finding
false annotations on a novel validation view, or using the latest deep learning
image analysis technologies. This is combined with a version control system
which makes it possible to keep track of changes in the data sets and, for
example, to link the results of deep learning experiments to specific data set
versions. EXACT is freely available and has already been successfully applied
to a broad range of annotation tasks, including highly diverse applications
like deep learning supported cytology scoring, interdisciplinary multi-centre
whole slide image tumour annotation, and highly specialised whale sound
spectroscopy clustering.
Related papers
- Language Guided Domain Generalized Medical Image Segmentation [68.93124785575739]
Single source domain generalization holds promise for more reliable and consistent image segmentation across real-world clinical settings.
We propose an approach that explicitly leverages textual information by incorporating a contrastive learning mechanism guided by the text encoder features.
Our approach achieves favorable performance against existing methods in literature.
arXiv Detail & Related papers (2024-04-01T17:48:15Z) - MouSi: Poly-Visual-Expert Vision-Language Models [132.58949014605477]
This paper proposes the use of ensemble experts technique to synergize the capabilities of individual visual encoders.
This technique introduces a fusion network to unify the processing of outputs from different visual experts.
In our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1.
arXiv Detail & Related papers (2024-01-30T18:09:11Z) - Domain Generalization for Mammographic Image Analysis with Contrastive
Learning [62.25104935889111]
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
A novel contrastive learning is developed to equip the deep learning models with better style generalization capability.
The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
arXiv Detail & Related papers (2023-04-20T11:40:21Z) - 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) - Multiple Instance Learning for Digital Pathology: A Review on the
State-of-the-Art, Limitations & Future Potential [0.29008108937701327]
Digital whole slides images contain an enormous amount of information.
Deep neural networks show high potential with respect to various tasks in the field of digital pathology.
Deep learning algorithms require (manual) annotations in addition to the large amounts of image data to enable effective training.
Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data.
arXiv Detail & Related papers (2022-06-09T11:27:26Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - Generalized Multi-Task Learning from Substantially Unlabeled
Multi-Source Medical Image Data [11.061381376559053]
MultiMix is a new multi-task learning model that jointly learns disease classification and anatomical segmentation in a semi-supervised manner.
Our experiments with varying quantities of multi-source labeled data in the training sets confirm the effectiveness of MultiMix.
arXiv Detail & Related papers (2021-10-25T18:09:19Z) - Federated Learning for Computational Pathology on Gigapixel Whole Slide
Images [4.035591045544291]
We introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology.
We evaluate our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels.
arXiv Detail & Related papers (2020-09-21T21:56:08Z) - Advances in Deep Learning for Hyperspectral Image Analysis--Addressing
Challenges Arising in Practical Imaging Scenarios [7.41157183358269]
We will review advances in the community that leverage deep learning for robust hyperspectral image analysis.
challenges include limited ground truth and high dimensional nature of the data.
Specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis.
arXiv Detail & Related papers (2020-07-16T19:51:02Z) - Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
Image segmentation is a key topic in image processing and computer vision.
There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
arXiv Detail & Related papers (2020-01-15T21:37:47Z)
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.