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Open Access

Correction

Correction: A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology

  • Duhita Sengupta,
  • Sk Nishan Ali,
  • Aditya Bhattacharya,
  • Joy Mustafi,
  • Asima Mukhopadhyay,
  • Kaushik Sengupta

There is an error in affiliation 2 for the author Duhita Sengupta. The correct affiliation 2 is: Homi Bhabha National Institute, Mumbai, India

An additional affiliation is missing for the sixth author. Kaushik Sengupta is also affiliated with Homi Bhabha National Institute, Mumbai, India.

Reference

  1. 1. Sengupta D, Ali SN, Bhattacharya A, Mustafi J, Mukhopadhyay A, Sengupta K (2022) A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. PLoS ONE 17(1): e0261181. pmid:34995293

Citation: Sengupta D, Ali SN, Bhattacharya A, Mustafi J, Mukhopadhyay A, Sengupta K (2024) Correction: A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. PLoS ONE 19(12): e0316114. https://doi.org/10.1371/journal.pone.0316114

Published: December 17, 2024

Copyright: © 2024 Sengupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.