Updates are available Correction dated 2023-06-22

Article History

Received: 6 June 2022

Revised: 11 February 2023

Accepted: 6 March 2023

First Online: 20 May 2023

Change Date: 22 June 2023

Change Type: Correction

Change Details: A Correction to this paper has been published:

Change Details: https://doi.org/10.1007/s00330-023-09851-2

Declarations

:

: The scientific guarantor of this publication is Dr Krithika Rangarajan, Assistant Professor, Radiology, AIIMS, New Delhi.

: The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

: Dr M. Kalaivani, Professor, Department of Biostatistics, AIIMS kindly provided statistical advice for this manuscript.

: Informed consent was waived by the institutional review board.

: Institutional Review Board approval was obtained. (IEC-247-4.05.2018). The title of the project for which ethical clearance was obtained is “Deep learning for detection and classification of abnormalities on full field digital mammography.” The current manuscript is one of the works done under this project.

: Mammograms from study subjects are being used for the development of other AI algorithms for cancer detection on mammograms as well. However, none of the other studies deals with the detection of cancers in dense breasts and uses completely different neural networks. The current neural network is not reported on the data presented in the manuscript in any other work.

: This was a retrospective diagnostic accuracy study performed at 2 centres of one institution. These centres were the Department of Radiology, All India Institute of Medical Sciences New Delhi (centre 1) and the Department of Oncoradiology, BR Ambedkar Institute Rotary Cancer Hospital, AIIMS (centre 2), New Delhi. The DM dataset and Institutional training dataset described in the manuscript were obtained from centre 1, and the SM dataset was obtained from centre 2. The original ethical clearance obtained from IRB was for mammography and pathology information from centre 1 (IEC-247–4.05.2018). Centre 2 mammography and pathology information was allowed by IRB as an addendum to the same ethical clearance (IEC-247–4.05.2018 OP03/04.06.2021). The other 2 datasets described are publicly available datasets which are downloadable. These can be accessed from:• The CBIS-DDSM dataset is available from • The INBreast dataset is available upon request from