mlsanityData to 14 Oct 2026

Sequential Hard Mining: a data-centric approach for Mitosis Detection

arXiv 2509.02588 ↗First posted 28 Aug 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

With a continuously growing availability of annotated datasets of mitotic figures in histology images, finding the best way to optimally use with this unprecedented amount of data to optimally train deep learning models has become a new challenge. Here, we build upon previously proposed approaches with a focus on efficient sampling of training data inspired by boosting techniques and present our candidate solutions for the two tracks of the MIDOG 2025 challenge.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 28 Aug 2025.

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7 of 11 references.

References · 7

  1. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  2. 2016Group Equivariant Convolutional Networkscited by 1,165
  3. 2022Mitosis domain generalization in histopathology images -- The MIDOG challengecited by 67
  4. 2020Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysiscited by 32
  5. 2023Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challengecited by 25
  6. 2021Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challengecited by 6
  7. 2023Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Datasetcited by 3

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