Stain Specific Standardization of Whole-Slide Histopathological Images

By Babak Ehteshami Bejnordi, Geert J. S. Litjens, N. Timofeeva, I. Otte-Höller, A. Homeyer, Nico Karssemeijer, J. A. van der Laak. 2016

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Medical Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yoshua Bengio
2. Max Welling
3. Sergey Levine
4. Mohit Bansal
5. Quoc V. Le
6. Ruslan Salakhutdinov
7. Jianfeng Gao
8. Trevor Darrell

In Biophysics

1. Max Welling
2. Faisal Mahmood
3. Ozan Ciga
4. Mohammad Norouzi
5. Subhashini Venugopalan
6. Taco Cohen
7. Richard J. Chen
8. Nicholas J. Durr

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Andrew Zisserman
3. Trevor Darrell
4. Dacheng Tao
5. Jitendra Malik
6. Andrea Vedaldi
7. Xiaogang Wang
8. Ming-Hsuan Yang

In Medical Imaging

1. Faisal Mahmood
2. Richard J. Chen
3. Anne L. Martel
4. Ozan Ciga
5. Chetan L. Srinidhi
6. Francesco Ciompi
7. J. A. van der Laak
8. Geert J. S. Litjens

Citations in the corpus

• Rotation Equivariant CNNs for Digital Pathology (2018, background): “This approach has been shown to outperform pathologists in a variety of tasks[1,2,3].”
• A survey on deep learning in medical image analysis (2017, background): “To the best of our knowledge, the multi-stream multi-scale architecture was first explored by Farabet et al. (2013) , who used it for segmentation in natural images.”
• Detecting Cancer Metastases on Gigapixel Pathology Images (2017, methodology): “4% respectively [1] using color normalization [4], additional data augmentation, and lowering the inference stride from 64 to 4.”
• The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “The automated extraction of the matrix W has been done by using: (i) prior information of the stain vectors [52], (ii) singular value decomposition [51,53] (iii) support vector machines [25], (iv) gaussian mixture model [54] and (v) cellular structures segmentation [55 – 57].”
• Deep neural network models for computational histopathology: A survey (2019, methodology): “, 2019b) in AUC for a binary classification task on WSIs) utilizes a traditional machine learning based normalization technique that aligns chromatic and density distributions of source and the target (Bejnordi et al., 2015).”
• Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, methodology): “Several methods have been presented for normalizing pathology images or adapting networks trained on data from one site to data from other locations [55, 57–59].”
• From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge (2019, background): “Two teams took a completely different approach and tried to use stain normalization algorithms [26] to ensure a uniform color distribution across the images.”
• Machine Learning Methods for Histopathological Image Analysis (2017, background): “[66] Bejnordi BE, Litjens G, Timofeeva N, Otte-Höller I, Homeyer A, Karssemeijer N, et al.”
• Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. (2018, methodology): “This is a simplified version of the approach described by Bejnordi et al.(19) For”