This dataset consists of 361 whole slide images (WSI) - 296 malignant from women with invasive breast cancer (HER2 neg) and 65 benign. The tumours have been classified with four SNOMED-CT categories based on morphology: invasive duct carcinoma, invasive lobular carcinoma, in situ carcinoma, and others. 4144 separate annotations have been made to segment different tissue structures connected to ontologies.
Keywords: Pathology, Breast, Cancer, Whole slide imaging, Annotated.
Sample images with reduced image quality. Please click to preview.
|Cite as||Anna Bodén, Jerónimo F. Rose, Martin Lindvall, and Caroline Bivik Stadler (2019) Breast data from the Visual Sweden project DROID doi:10.23698/aida/drbr|
|Title||Breast data from the Visual Sweden project DROID|
Jerónimo F. Rose
Caroline Bivik Stadler
|Resolution||40X single plane, scanned in NDP format|
Hamamatsu NanoZoomer-XR C12000 series 2013
Hamamatsu NanoZoomer 2.0 HT C9600 series 2013
|Stain||H&E (hematoxylin and eosin)|
AIDA BY license
|Copyright||Copyright 2019 Linköping University, Claes Lundström|
|Download||Please contact Anna Bodén, Claes Lundstrom, or Joel Hedlund to request access.|
The breast tumours were classified with four SNOMED-CT categories based on morphology: invasive duct carcinoma, Invasive lobular carcinoma, in situ carcinoma and others. 4144 separate annotations were made to segment different tissue structures connected to ontologies. One physician were responsible for the manual annotations controlled by a second pathologist.
Free for use in legal and ethical medical diagnostics research. Please contact the copyright holder for terms of access.
AIDA BY license
Copyright 2019 Linköping University, Claes Lundström
Permission to use, copy, modify, and/or distribute this data within Analytic Imaging Diagnostics Arena (AIDA) for the purpose of medical imaging research with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies, and that publications resulting from the use of this data cite the following works:
Anna Bodén, Jerónimo F. Rose, Martin Lindvall, and Caroline Bivik Stadler (2019) Breast data from the Visual Sweden project DROID doi:10.23698/aida/drbr.
Stadler, C.B., Lindvall, M., Lundström, C. et al. Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training. J Digit Imaging (2020). https://doi.org/10.1007/s10278-020-00384-4
THE DATA IS PROVIDED “AS IS” AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS DATA INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR CHARACTERISTICS OF THIS DATA.