A curated mammography data set for use in computer-aided detection and diagnosis research

By R. Lee, Francisco Gimenez, Assaf Hoogi, K. Miyake, Mia Gorovoy, Daniel L. Rubin. Scientific Data, 2017

812 citations recorded by the source corpus.

Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; 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. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography. Design Type(s) parallel group design • feature extraction objective • image processing objective Measurement Type(s) Mammography Technology Type(s) digital curation Factor Type(s) diagnosis Sample Characteristic(s) Homo sapiens • breast Design Type(s) parallel group design • feature extraction objective • image processing objective Measurement Type(s) Mammography Technology Type(s) digital curation Factor Type(s) diagnosis Sample Characteristic(s) Homo sapiens • breast Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6fec0449883d564cefc818fd3d087a4193f2db5d

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In Artificial Intelligence

In Oncology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

This Looks Like That: Deep Learning for Interpretable Image Recognition (2018, methodology): “We trained and evaluated our interpretable network using the CBIS-DDSM dataset [6, 17, 16].”
Breast cancer detection using deep convolutional neural networks and support vector machines (2019, methodology): “Moreover, a new dataset is presented in this work, which is the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) (Lee et al., 2017).”
Preparing Medical Imaging Data for Machine Learning (2020, background): “Accumulation of this clinical information for a large number of patients can be resource intensive and is often referred to as electronic phenotyping (36).”
Transformers in Medical Imaging: A Survey (2022, methodology): “[33] Multi-organ Mammograms Dermoscopy 2D APTOS-2019 [255] ISIC-2019 [256] CBIS-DDSM [257] Recall AuC Pure Systemetic study of whether one should replace CNNs with ViTs for medical image classification.”
A generalist vision–language foundation model for diverse biomedical tasks (2023, neutral): “Additionally, we benchmarked BiomedGPT against Med-PaLM M on the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) dataset 41 for both three-class lesion-level mass classification and calcification classification.”