13 datasets found

The Cancer Genome Atlas (TCGA) Lung Cancer dataset comprises two cancer subtypes: Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). It includes diagnostic...

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices.

CAMELYON-17 consists of 145 positive slides and 353 negative slides, where positive patches occupying less than 10% of the tissue area in positive slides.

A large-scale lung nodule dataset with pathology- or follow-up-confirmed benign/malignant labels for low-dose and noncontrast CTs.

The TCGA-NSCLC dataset includes two sub-types of lung cancer, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC).

The dataset used in this paper includes Whole Slide Images (WSIs) and reports (paired together) of celiac disease, lung cancer and colon cancer, collected from two hospitals:...

The dataset is used for classification of histopathology images of lung cancer using Convolutional Neural Network (CNN).

A dataset for pulmonary nodule detection with 4146 CT scans and 8 categories of pulmonary nodules.

Reduced lung-cancer mortality with low-dose computed tomographic screening.

A dataset for validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) dataset is used for training and testing a ResNet-50 model to classify CT scans of lungs...

MIMIC-III is a large, publicly available clinical database containing information from the Medical Information Mart for Intensive Care III. It is used for various clinical...

The Lung Cancer part of the dataset LC25000 has 25,000 color images in 5 classes. Each class contains 5,000 images of the histologic categories: colon adenocarcinoma, benign...