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Preparing data for pathological artificial intelligence with clinical-grade performance

arXiv 2205.10748 ↗First posted 22 May 2022eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

[Purpose] The pathology is decisive for disease diagnosis, but relies heavily on the experienced pathologists. Recently, pathological artificial intelligence (PAI) is thought to improve diagnostic accuracy and efficiency. However, the high performance of PAI based on deep learning in the laboratory generally cannot be reproduced in the clinic. [Methods] Because the data preparation is important for PAI, the paper has reviewed PAI-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. The in-depth analysis of methods for preparing data is performed, including obtaining slides of pathological tissue, cleaning, screening, and then digitizing. Expert review, image annotation, dataset division for model training and validation are also discussed. We further discuss the reasons why the high performance of PAI is not reproducible in the clinical practices and show some effective ways to improve clinical performances of PAI. [Results] The robustness of PAI depend on randomized collection of representative disease slides, including rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and the amount of data. The digital pathology is fundamental of clinical-grade PAI, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. [Conclusion] The representative data, the amount of labeling and consistency from multi-centers is the key to performance reproduction. The digital pathology for clinical diagnosis, data standardization and technique of WSI-based weakly supervised learning hopefully build clinical-grade PAI. Keywords: pathological artificial intelligence; data preparation; clinical-grade; deep learning

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 118 references that are in the map. First posted on arXiv 22 May 2022.

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References · 21

  1. 2022Generative Adversarial Networkscited by 5,627
  2. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  3. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  4. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  5. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  6. 2019Automated Gleason Grading of Prostate Biopsies using Deep Learningcited by 69
  7. 2019Deep Learning for Whole Slide Image Analysis: An Overviewcited by 51
  8. 2020A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysiscited by 45
  9. 2019Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networkscited by 43
  10. 2021Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labelscited by 43
  11. 2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
  12. 2019Resolving challenges in deep learning-based analyses of histopathological images using explanation methodscited by 32
9 more
  1. 2019Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approachcited by 27
  2. 2020Detection of prostate cancer in whole-slide images through end-to-end training with image-level labelscited by 19
  3. 2020Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Typescited by 17
  4. 2017Unsupervised Histopathology Image Synthesiscited by 11
  5. 2020Single-shot autofocusing of microscopy images using deep learningcited by 11
  6. 2018Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathologycited by 7
  7. 2019Simple yet Effective Way for Improving the Performance of GANcited by 5
  8. 2018An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarraycited by 4
  9. 2020Automated Detection of Cribriform Growth Patterns in Prostate Histology Imagescited by 4

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