Localtab |
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active | true |
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title | Data Access |
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| Data Access
Data Type | Download all or Query/Filter | License |
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Slide Images (JPG, 196MB) |
Tcia button generator |
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url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/70?passcode=93cc61a4c4ae53aa503b26d8c87badd592e75fd2?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU0NyIsInBhc3Njb2RlIjoiZWFkN2JhZTJjNTVlZjZkOWNjYzVhY2QyNTA5NGY0MjQ5OWIwNDA3OCIsInBhY2thZ2VfaWQiOiI1NDciLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
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Tcia button generator |
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label | Search |
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url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:osteosarcoma_tumor_assessment |
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(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | Features (CSV, 860 kB) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/52756935/ML_Features_1144.csv?version=1&modificationDate=1613057224752&api=v2 |
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Click the Versions tab for more info about data releases.
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Localtab |
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title | Detailed Description |
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| Detailed Description | |
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Modalities | Pathology | Number of Participants | 4 | Number of Images | 1144 | Images Size (MB) | 196 | Folder_Structure- Data_Osteo_Files
- ML_Features_1144.csv - Contains 1144 rows for all the image tiles and 69 columns for filename, classification, and 65 machine learning features.
- Training_Set_1 - 11 folders with 547 images. Each folder contains 48~50 image tiles and 1 csv for annotation.
- set 1- 49 Image Tiles
- set 2- 50 Image Tiles
- set 3- 50 Image Tiles
- set 4- 50 Image Tiles
- set 5- 50 Image Tiles
- set 6- 50 Image Tiles
- set 7- 50 Image Tiles
- set 8- 50 Image Tiles
- set 9- 50 Image Tiles
- set 10- 50 Image Tiles
- set 11- 48 Image Tiles
- Training_Set_2 - 12 folders with 597 images. Each folder contains 48~50 image tiles and 1 csv for annotation.
- set 1- 49 Image Tiles
- set 2- 50 Image Tiles
- set 3- 50 Image Tiles
- set 4- 50 Image Tiles
- set 5- 50 Image Tiles
- set 6- 50 Image Tiles
- set 7- 50 Image Tiles
- set 8- 50 Image Tiles
- set 9- 50 Image Tiles
- set 10- 50 Image Tiles
- set 11- 50 Image Tiles
- set 12- 48 Image Tiles
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Localtab |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia limited license policy |
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Info |
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| Leavey, P., Sengupta, A., Rakheja, D., Daescu, O., Arunachalam, H. B., & Mishra, R. (2019). Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.bvhjhdas |
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title | Publication Citation |
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| Mishra, R., Daescu, O., Leavey, P., Rakheja, D., & Sengupta, A. (2017). Histopathological Diagnosis for Viable and Non-viable Tumor Prediction for Osteosarcoma Using Convolutional Neural Network. In Bioinformatics Research and Applications (pp. 12–23). Springer International Publishing. https://doi.org/10.1007/978-3-319-59575-7_2 |
Info |
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title | Publication Citation |
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| Arunachalam, H. B., Mishra, R., Armaselu, B., Daescu, O., Martinez, M., Leavey, P., Rakheja, D., Cederberg, K., Sengupta, A., & Ni’suilleabhain, M. (2016). COMPUTER AIDED IMAGE SEGMENTATION AND CLASSIFICATION FOR VIABLE AND NON-VIABLE TUMOR IDENTIFICATION IN OSTEOSARCOMA. In Biocomputing 2017. Proceedings of the Pacific Symposium. WORLD SCIENTIFIC. https://doi.org/10.1142/9789813207813_0020 |
Info |
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title | Publication Citation |
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| Mishra, R., Daescu, O., Leavey, P., Rakheja, D., & Sengupta, A. (2018). 1) Mishra, Rashika, et al. "Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network." International Symposium on Bioinformatics Research and Applications. Springer, Cham, 2017. 2) Arunachalam, Harish Babu, et al. "Computer aided image segmentation and classification for viable and non-viable tumor identification in osteosarcoma." PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017. 2017. 3) Mishra, Rashika, et al. "Convolutional Neural Network for Histopathological Analysis of Osteosarcoma. " In Journal of Computational Biology (Vol. 25.3 (2018): 313-325., Issue 3, pp. 313–325). Mary Ann Liebert Inc. https://doi.org/10.1089/cmb.2017.0153 |
Info |
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title | Publication Citation |
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| Leavey, P., Arunachalam, H.B., Armaselu, B., Sengupta, A., Rakheja, D., Skapek, S., Cederberg, K., Bach, J.P., Glick, S., Ni'Suilleabhain, M. and Mishra, R., 4) Leavey, Patrick, et al. "Implementation of Computer-Based Image Pattern Recognition Algorithms to Interpret Tumor Necrosis; a First Step in Development of a Novel Biomarker in Osteosarcoma." PEDIATRIC BLOOD & CANCER. Vol. 64. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY, 2017. |
Info |
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| 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 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA our data. If you have a manuscript you'd like to add please contact the TCIA's Helpdesk. |
Localtab |
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| Version 1 (Current): Updated 2019/03/22
Data Type | Download all or Query/Filter |
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Images (JPG, 196MB) |
Tcia button generator |
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url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/70?passcode=93cc61a4c4ae53aa503b26d8c87badd592e75fd2?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU0NyIsInBhc3Njb2RlIjoiZWFkN2JhZTJjNTVlZjZkOWNjYzVhY2QyNTA5NGY0MjQ5OWIwNDA3OCIsInBhY2thZ2VfaWQiOiI1NDciLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
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Tcia button generator |
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label | Search |
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url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:osteosarcoma_tumor_assessment |
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(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | Features (CSV) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/52756935/ML_Features_1144.csv?version=1&modificationDate=1613057224752&api=v2 |
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