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Article summary:
1. Histopathological tissue classification is a simpler way to achieve semantic segmentation for whole slide images, which can reduce the need for pixel-level dense annotations.
2. Pyramidal Deep-Broad Learning (PDBL) is proposed as a lightweight plug-and-play module for any well-trained classification backbone to improve the classification performance without re-training burden.
3. PDBL was tested on three CNN backbones and two datasets, and results showed that it can steadily improve the tissue-level classification performance for any CNN backbones, especially when given a small amount of training samples.
Article analysis:
The article “PDBL: Improving Histopathological Tissue Classification With Plug-and-Play Pyramidal Deep-Broad Learning” provides an overview of the proposed Pyramidal Deep-Broad Learning (PDBL) module and its application in histopathological tissue classification. The article is written in a clear and concise manner, providing sufficient evidence to support its claims. The authors have conducted extensive experiments on two datasets with three different CNN backbones to demonstrate the effectiveness of their proposed method. Furthermore, they have provided source code for their work, making it easier for other researchers to replicate their results.
In terms of trustworthiness and reliability, there are no obvious biases or unsupported claims in this article. All claims are supported by evidence from experiments conducted by the authors or from related works cited in the article. The authors have also considered potential risks associated with their work such as computational resources and annotation efforts required for training models using PDBL module. Moreover, all points of consideration are presented equally without any partiality or one sided reporting.
In conclusion, this article is reliable and trustworthy as it provides sufficient evidence to support its claims and presents both sides equally without any bias or partiality.