AI Article Synopsis
- The digital pathology field is rapidly growing, primarily due to the use of Whole Slide Images (WSIs) which support automated diagnostics.
- The paper reviews current methods in histopathology that help explain CNN (Convolutional Neural Network) classifications, providing valuable insights for histopathology professionals.
- There is a gap in trust and utilization of deep learning models among pathologists, and to foster sustainable use, it's crucial to help them understand how these models work and relate to their expertise.
Article Abstract
The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.
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| http://dx.doi.org/10.3233/SHTI240579 | DOI Listing |
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