儿童髓母细胞瘤分类数据集（CoMB-Deep表4）

数据集获取（Figshare）

• 源站标题：Table_4_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx
• DOI：10.3389/fninf.2021.663592.s006
• 发布日期：2021-05-28
• 文件数：1 个
• 文件总体积：15.91 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_CoMB-Deep_Composite_Deep_Learning-Based_Pipeline_for_Classifying_Childhood_Medulloblastoma_and_Its_Classes_docx/14692920

命令行下载

curl -L -o "Table_4_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx" "https://ndownloader.figshare.com/files/28217625"

文件清单

• Table_4_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx（15.91 KB）

文件总体积 15.91 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fninf.2021.663592.s006（source: https://frontiersin.figshare.com/articles/dataset/Table_4_CoMB-Deep_Composite_Deep_Learning-Based_Pipeline_for_Classifying_Childhood_Medulloblastoma_and_Its_Classes_docx/14692920）。