CT Volume Samples for Lung Adenocarcinoma Classification

Major content: This dataset consists of lung adenocarcinoma samples locally cropped from 3D CT images. Samples are discriminated by 3 pathological categories, respectively Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), and Minimally Invasive Adenocarcinoma (MIA). This dataset totally contains 21 samples of AAH, 444 samples of AIS, and 585 samples of MIA. Usage: Samples are stored with .npy files, as can be loaded in Python by the package of NumPy, and introduced in deep learning on the task of lung adenocarcinoma classification. Each sample is a 3D volume with 128*128*128 voxel resolution, where the lung adenocarcinoma is located at the center of the volume, and the volume can be cropped with any scale (e.g. 64*64*64) to fit the input size of 3D convolutional neural networks.数据集核心内容：本数据集包含从三维CT（3D CT）图像中局部裁剪得到的肺腺癌样本。样本依据3种病理分型进行划分，分别为不典型腺瘤样增生（Atypical Adenomatous Hyperplasia, AAH）、原位腺癌（Adenocarcinoma In Situ, AIS）以及微浸润性腺癌（Minimally Invasive Adenocarcinoma, MIA）。本数据集总计包含AAH样本21例、AIS样本444例以及MIA样本585例。数据集用途：样本以.npy文件格式存储，可通过Python的NumPy库加载，适用于肺腺癌分类任务的深度学习研究。每个样本为分辨率128×128×128体素的三维体数据，肺腺癌病灶位于该体数据的中心区域；用户可根据三维卷积神经网络的输入尺寸需求，以任意比例（如64×64×64）对该体数据进行裁剪以适配模型输入。

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于肺腺癌分类的CT体积样本集合，包含从3D CT图像中裁剪的1050个样本，覆盖AAH、AIS和MIA三种病理类别。样本以128*128*128体素分辨率的3D体积存储，支持通过NumPy加载，并可根据需要裁剪以适配3D卷积神经网络，适用于深度学习分类任务。数据集旨在支持医学影像分析，特别是肺腺癌的自动诊断研究。