cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.16490
title: CPIA Dataset_Part08: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part08: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part08 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part08 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.16489
title: CPIA Dataset_Part07: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part07: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part07 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part07 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.16488
title: CPIA Dataset_Part06: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part06: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part06 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part06 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.14727
title: CPIA Dataset_Part05: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part05: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part05 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part05 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.14711
title: CPIA Dataset_Part04: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part04: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part04 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part04 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.14710
title: CPIA Dataset_Part03: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part03: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part03 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part03 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.14658
title: CPIA Dataset_Part02: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
title: CPIA Dataset_Part02: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part02 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
description: Pathological image analysis is a crucial field in computer-aided diagnosis. Transfer learning using models initialized on natural images has improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, overcoming the challenge of expensive annotations. Thus, this field calls for a comprehensive dataset, similar to the ImageNet in computer vision. This paper presents a large-scale comprehensive pathological image analysis (CPIA) dataset for SSL pre-training. The CPIA dataset contains 148,962,579 images, covering over 48 organs/tissues and approximately 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). And we establish a multi-scale pathological data processing workflow, combined with the diagnosis habits of senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. This is the Part02 of CPIA dataset, including the CPIA-Mini and partial CPIA dataset. The related code and information are available at https://github.com/zhanglab2021/CPIA_Dataset.
DOI: 10.57760/sciencedb.space.00547
title: Modulation classification data based on RML2016.10a
title: Modulation classification data based on RML2016.10a

description: This data is reprocessed on the original data, that is, the original data is down-sampled, and the sampling rate becomes half of the original.
description: This data is reprocessed on the original data, that is, the original data is down-sampled, and the sampling rate becomes half of the original.
DOI: 10.57760/sciencedb.space.01916
title: Data from 3D electrostatic hybrid PIC simulation of the plasma mini-wake near a lunar polar crater
title: Data from 3D electrostatic hybrid PIC simulation of the plasma mini-wake near a lunar polar crater

description: The related data files for the manuscript "3D electrostatic hybrid PIC simulation of the plasma mini-wake near a lunar polar crater", which are obtained by the 3D PIC simulations with SPIS code. There are 17 data files and 5 figures in total. 2 of the 17 data files are in .vtk format, which are related to the Figure 1 and can be opened and edited by ParaView software. The left 15 data files are in .dat format, which are related to the Figures 2-5, and can be edited by Tecplot software.
description: The related data files for the manuscript "3D electrostatic hybrid PIC simulation of the plasma mini-wake near a lunar polar crater", which are obtained by the 3D PIC simulations with SPIS code. There are 17 data files and 5 figures in total. 2 of the 17 data files are in .vtk format, which are related to the Figure 1 and can be opened and edited by ParaView software. The left 15 data files are in .dat format, which are related to the Figures 2-5, and can be edited by Tecplot software.