Cancer

The dataset consists of 400 whole-slide images (WSIs) of lymph node sections stained with hematoxylin and eosin (H&E), collected from two medical centers in the Netherlands. The WSIs are stored in a multi-resolution pyramid format, allowing for efficient retrieval of image subregions at different magnification levels. The training set includes two subsets: - 170 WSIs (100 normal, 70 with metastases) from Radboud University Medical Center - 100 WSIs (60 normal, 40 with metastases) from University Medical Center Utrecht The test set consists of 130 WSIs from both institutions. Ground truth data for metastases is provided as XML files with annotated contours and WSI binary masks. The Camelyon16 dataset aims to reduce the workload and subjectivity in cancer diagnosis by pathologists. It serves as a benchmark for evaluating algorithms that can automatically detect metastases in histopathological images, focusing on breast cancer in sentinel lymph nodes. Researchers can develop and refine machine learning models for automated detection of metastases. The dataset allows for performance comparisons of different detection algorithms. Automated systems can be integrated into clinical workflows to enhance diagnostic accuracy and efficiency. The dataset is valuable for training medical professionals in digital pathology and AI applications in diagnostics.

BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories.

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels (0, 1+, 2+, 3+).

Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively. This noisy dataset is constructed in the real scenario. - Details: Colon slides from Chaoyang hospital, the patch size is 512 × 512. We invited 3 professional pathologists to label the patches, respectively. We took the parts of labeled patches with consensus results from 3 pathologists as the testing set. Others we used as the training set. For the samples with inconsistent labeling opinions of the three doctors in the training set (this part accounts for about 40%), we randomly selected the opinions from one of the three doctors. - The original WSIs are scanned at X20 objective magnification.

This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the Digital Database for Screening Mammography (DDSM) . The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer. The images have been decompressed and converted to DICOM format. Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. A manuscript describing how to use this dataset in detail is available at https://www.nature.com/articles/sdata2017177. Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility. For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Please note that the image data for this collection is structured such that each participant has multiple patient IDs. For example, participant 00038 has 10 separate patient IDs which provide information about the scans within the IDs (e.g. Calc-Test_P_00038_LEFT_CC, Calc-Test_P_00038_RIGHT_CC_1). This makes it appear as though there are 6,671 patients according to the DICOM metadata, but there are only 1,566 actual participants in the cohort.

The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil. Paper: F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264 Source: https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/ Image Source: https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/

The dataset contains 33,126 dermoscopic training images of unique benign and malignant skin lesions from over 2,000 patients. Each image is associated with one of these individuals using a unique patient identifier. All malignant diagnoses have been confirmed via histopathology, and benign diagnoses have been confirmed using either expert agreement, longitudinal follow-up, or histopathology. A thorough publication describing all features of this dataset is available in the form of a pre-print that has not yet undergone peer review. The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital Clínic de Barcelona, Medical University of Vienna, Memorial Sloan Kettering Cancer Center, Melanoma Institute Australia, University of Queensland, and the University of Athens Medical School. The dataset was curated for the SIIM-ISIC Melanoma Classification Challenge hosted on Kaggle during the Summer of 2020. DOI: https://doi.org/10.34970/2020-ds01

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as (1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal A, luminal B, HER2 positive, and Triple-negative. However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types. To help fill the gap, we built a database conducted on 1,775 patients from China with benign or malignant breast disease who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image data were acquired on a GE Senographe DS mammography system. Publication Citation Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., Chen, J., & Li, L. (2019). Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Computational and Mathematical Methods in Medicine, 2019, 1–10. https://doi.org/10.1155/2019/2717454 Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016). Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6(1). https://doi.org/10.1038/srep27327 Data Citation Cui, Chunyan; Li Li; Cai, Hongmin; Fan, Zhihao; Zhang, Ling; Dan, Tingting; Li, Jiao; Wang, Jinghua. (2021) The Chinese Mammography Database (CMMD): An online mammography database with biopsy confirmed types for machine diagnosis of breast. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.eqde-4b16 TCIA Citation Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Three-dimensional position of external markers placed on the chest and abdomen of healthy individuals breathing during intervals from 73s to 222s. The markers move because of the respiratory motion, and their position is sampled at approximately 10Hz. Markers are metallic objects used during external beam radiotherapy to track and predict the motion of tumors due to breathing for accurate dose delivery. The same data was used and described in detail in the following article: Krilavicius, Tomas, et al. “Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy.” ArXiv:1508.00749 [Physics], Aug. 2015. arXiv.org, http://arxiv.org/abs/1508.00749.

The breast lesion detection in ultrasound videos dataset uses a clip-level and video-level feature aggregated network (CVA-Net) and consists of 188 ultrasound videos, of which 113 are labeled malignant and 75 benign. Overall these consist of 25,272 ultrasound images in total with the number of images for each video varying from 28 to 413. 150 videos were used for training, 38 for testing. The primary intended use case would be for computer-aided breast cancer diagnosis, supporting systems to assist radiologists. Here are more details summarising the approach: * A novel network: a new state-of-the-art clip-level and video level feature aggregated network (CVA-Net) created to aggregate clip-level temporal features and video-level lesion classification features to fuse and into a prediction classifier. It outperformed existing methods mainly focused on 2D images and or fusing with unlabeled videos. * The need for increased accuracy contributed to the motivation, given detection challenges due to blurry breast lesion boundaries, inhomogeneous distributions, changeable breast lesion sizes, and positions in dynamic video. * Each video has a complete scan of the tumor, from where it becomes visible to where it is no longer visible as well as the largest section - all acquired via LOGIQ-E9 and PHILIPS TIS L9-3 ultrasound machines . * 2 pathologists with 8 years of experience invited to manually annotate the breast lesion rectangles inside each frame and give the corresponding classification. Second source in addition to separate homepage URL below: https://github.com/jhl-Det/CVA-Net/tree/main/datasets

Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification. Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist. Results: The dataset contains 1613 CT volumes with 2487 annotated nodules. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%. Conclusions: The Duke Lung Nodule Dataset is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.

The National Lung Screening Trial (NLST) was a randomized controlled trial conducted by the Lung Screening Study group (LSS) and the American College of Radiology Imaging Network (ACRIN) to determine whether screening for lung cancer with low-dose helical computed tomography (CT) reduces mortality from lung cancer in high-risk individuals relative to screening with chest radiography. Approximately 54,000 participants were enrolled between August 2002 and April 2004. Data collection has ended, and information is complete through December 31, 2009. NLST has the ClinicalTrials.gov registration number NCT00047385.

The dataset is composed of Hematoxylin and eosin (H&E) stained breast histology microscopy and whole-slide images. Challenge participants should evaluate the performance of their method on either/both sets of images.