Learning from crowds for automated histopathological image segmentation Article Swipe
Miguel López-Pérez
,
Pablo Morales-Álvarez
,
Lee Cooper
,
Christopher Felicelli
,
Jeffrey A. Goldstein
,
Brian Vadasz
,
Rafael Molina
,
Aggelos K. Katsaggelos
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compmedimag.2024.102327
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compmedimag.2024.102327
Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged as a promising solution to reduce the individual (expert) annotation cost by distributing the labeling effort among a group of (non-expert) annotators. Extracting knowledge in this scenario is challenging, as it involves noisy annotations. Jointly learning the underlying (expert) segmentation and the annotators' expertise is currently a commonly used approach. Unfortunately, this approach is frequently carried out by learning a different neural network for each annotator, which scales poorly when the number of annotators grows. For this reason, this strategy cannot be easily applied to real-world CPATH segmentation. This paper proposes a new family of methods for CR segmentation of histopathological images. Our approach consists of two coupled networks: a segmentation network (for learning the expert segmentation) and an annotator network (for learning the annotators' expertise). We propose to estimate the annotators' behavior with only one network that receives the annotator ID as input, achieving scalability on the number of annotators. Our family is composed of three different models for the annotator network. Within this family, we propose a novel modeling of the annotator network in the CR segmentation literature, which considers the global features of the image. We validate our methods on a real-world dataset of Triple Negative Breast Cancer images labeled by several medical students. Our new CR modeling achieves a Dice coefficient of 0.7827, outperforming the well-known STAPLE (0.7039) and being competitive with the supervised method with expert labels (0.7723). The code is available at https://github.com/wizmik12/CRowd_Seg.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compmedimag.2024.102327
- OA Status
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- Cited By
- 6
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390610791
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390610791Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.compmedimag.2024.102327Digital Object Identifier
- Title
-
Learning from crowds for automated histopathological image segmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-04Full publication date if available
- Authors
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Miguel López-Pérez, Pablo Morales-Álvarez, Lee Cooper, Christopher Felicelli, Jeffrey A. Goldstein, Brian Vadasz, Rafael Molina, Aggelos K. KatsaggelosList of authors in order
- Landing page
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https://doi.org/10.1016/j.compmedimag.2024.102327Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.compmedimag.2024.102327Direct OA link when available
- Concepts
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Computer science, Segmentation, Artificial intelligence, Machine learning, Deep learning, Task (project management), Crowdsourcing, Artificial neural network, Image segmentation, Pattern recognition (psychology), Management, World Wide Web, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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