Fwd: The Cancer Imaging Archive Updates: April 2021

[Michelle Tacconelli]

The Cancer Imaging Archive Updates: April 2021 TCIA Partners with the The American College of Radiology Data Science Institute TCIA Partners with the The American College of Radiology Data Science Institute The American College of Radiology (ACR) Data Science Institute (DSI) and the Cancer Imaging Archive (TCIA) have teamed up to connect use cases and datasets to speed medical imaging artificial intelligence (AI) development. Dataset collection is the most important step in developing robust AI algorithms. Linking ACR DSI Define-AI use cases to datasets enables developers to build radiology AI algorithms that include defined data elements and DICOM images useful for inputs, outputs, and training and testing models. These links to relevant TCIA data can be found at the bottom of each Use Case page.  Read the full press release here. TCIA Collection Updates QIBA Anthropomorphic Abdominal Phantom CT Scans QIBA Anthropomorphic Abdominal Phantom CT Scans (QIBA-CT-Liver-Phantom) TCIA has a new liver phantom database, QIBA Anthropomorphic Abdominal Phantom CT Scans (QIBA-CT-Liver-Phantom).  This database contains a collection of three sets of CT scan images acquired from an anthropomorphic abdominal phantom with removable liver inserts. The anthropomorphic phantoms were designed by a group of scientists from FDA and Columbia University Medical Center and custom manufactured by QRM. The Chinese Mammography Database Chinese Mammography Database (CMMD) TCIA is excited to announce the publication of The Chinese Mammography Database (CMMD)! Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular subtypes. To help fill the gap, this data set contains 1,775 patients from China with benign or malignant classification labels. 749 of these subjects also include molecular subtype labels.   Data Analysis on TCIA TCIA has added a new "Data Analysis Center" to our list. Zegami helps find patterns in imaging datasets to assist with providing explainability of your Machine Learning models. See a demo using our CBIS-DDSM collection here.  Zegami The Cancer Imaging Archive (TCIA) The Cancer Imaging Archive offers a comprehensive archive of cancer images. Check out these video tutorials to learn how to contribute your imaging data and access our publicly available datasets. Stay Connected with The Cancer Imaging Archive  Manage Subscriptions | Help The Cancer imaging Archive is funded in part by Frederick Natational Lab for Cancer Research.

 

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