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Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

arXiv 2304.09314 ↗First posted 18 Apr 2023cs.CVcited by 0

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Abstract (arXiv)

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classification

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 18 Apr 2023.

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14 of 40 references.

References · 14

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  9. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  10. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
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  12. 2017Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networkscited by 17
2 more
  1. 2018Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathologycited by 17
  2. 2019Multi-Stage Pathological Image Classification using Semantic Segmentationcited by 13

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