Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

By Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha. bioRxiv, 2017

314 citations recorded by the source corpus.

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize various computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline utilizes a basic architecture of CNN, Google’s Inceptions within three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. These strategies include training the last layer of Google’s Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google’s Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google’s Inceptions even in presence of extensive tumor heterogeneity. Our pipeline on average achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer types, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-lab/CNN_Smoothie.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/bd6f783022ebd6704ff34f6bf824ef1cb1ad0cee

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. Yan Sun
2. Pegah Khosravi
3. Shuhao Wang
4. Xiangchun Li
5. Meng Yang
6. Sohrab P. Shah
7. Yahui Jiang

In Computer Vision and Pattern Recognition

1. Pegah Khosravi
2. Sohrab P. Shah

In Neurology

1. R. DePinho

Citations in the corpus

• Emerging role of deep learning‐based artificial intelligence in tumor pathology (2020, background): “Another system equipped with six state-of-the-art DL architectures has the potential to determine tumor, subtypes, as well as biomark-ers’ score in lung, breast, and bladder cancer [79].”
• Harnessing multimodal data integration to advance precision oncology (2021, methodology): “In histological imaging, similar computational models have advanced biomarker identification, particularly from haematoxylin and eosin (H&E)- stained whole slide images (WSIs) 28–32 , beyond the previously dominant practice of using pathologist-extracted features 33 .”
• Genetics and biology of prostate cancer (2018, background): “2018) and the accurate discrimination of various cancer tissues, cancer subtypes, biomarkers, and immunohistochemical scores (Khosravi et al. 2018).”