The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

By K. W. Clark, Bruce A. Vendt, Kirk E. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, Stanley R. Phillips, David R. Maffitt, Michael Pringle and 2 more. Journal of digital imaging, 2013

4,444 citations recorded by the source corpus.

Subfields: Information Systems and Management, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/251c39c1b9372f3055cd53d0001fc122bfb2e418

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Arti ﬁ cial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success (methodology): “The technical basis for accomplishing this already exists through networks created by the RSNA and the ACR as well as work such as The Cancer Imaging Archive at the National Cancer Institute to establish systems for image sharing [4].”
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].”
Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation (2018, methodology): “…CT images with expert delineated tumors from 377 patients with non-small cell lung cancer (NSCLC) [16] available from The Cancer Imaging Archive (TCIA) [17], and an unrelated cohort of 6 patients scanned with T2w MRI at our clinic before and during treatment every week (n=7) with radiation therapy.”
Artificial intelligence in radiology (2018, positive): “Nevertheless, public repositories such as The Cancer Imaging Archive (TCIA) 100 offer unparalleled open-access to labelled medical imaging data, allowing immediate AI model prototyping and thus eliminating lengthy data curation steps.”
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2024, methodology): “Another 50 cases from different medical centers [8] were used for evaluation and the annotations were provided by the challenge organizers.”
Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, neutral): “From 2011 to 2015, a much larger data set LIDC [18], [19] was developed.”
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules (2017, methodology): “Experimental results on the public LIDC-IDRI dataset [6,7] demonstrate that, despite the largely reduced amount of annotations required for training, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a CNN-based fully-supervised segmentation method.”
SynthStrip: skull-stripping for any brain image (2022, positive): “The QIN (Clark et al., 2013; Mamonov and Kalpathy-Cramer, 2016; Prah et al., 2015) dataset comprises pre-contrast, clinical stacks of thick image slices from patients with newly diagnosed glioblastoma.”
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models (2021, methodology): “Second, the Low Dose CT Image and Projection dataset (LDCT) (Clark et al., 2013; Moen et al., 2021) involves CT images of multiple anatomic sites, including 99 head CT scans, 100 chest CT scans, and 100 abdomen CT scans.”
Convolutional neural networks: an overview and application in radiology (2018, background): “Figure 12a shows a representative example of segmentation of the uterus with a malignant tumor on MRI [24, 44, 45].”
Radiomics: the facts and the challenges of image analysis (2018, background): “These images can be used as valuable sources for both hypothesis generating and validation purposes [26].”
AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (2018, methodology): “3) DATASET 3 (177 samples) consists of CT images from four different institutions in Québec, Canada [37], also downloaded from TCIA [36].”
Metrics Reloaded: Recommendations for image analysis validation (2022, neutral): “9 [12, 75] • Mitosis detection in histopathology images [7] • Lung nodule detection in CT images [4, 5, 26]”
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification (2018, background): “One exception we noticed is their experiment on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) [44–46], a medical image classiﬁcation task whose data is expensive to collect besides labeling.”