Deep neural network models for computational histopathology: A survey.
Srinidhi Chetan L,Ciga Ozan,Martel Anne L
Medical image analysis
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis. From the survey of over 130 papers, we review the field's progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer learning and various other sub-variants of these methods. We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks. Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.
Next generation pathology: artificial intelligence enhances histopathology practice.
Acs Balazs,Hartman Johan
The Journal of pathology
Deep learning algorithms have shown benefits for pathology in the context of risk stratification of tumors. Although the results are promising, several steps have to be made to confirm clinical utility. In a recent issue of The Journal of Pathology, Colling et al present a perspective manuscript providing a roadmap to routine use of artificial intelligence in histopathology. In this commentary, we aimed to put these key points in the context of recent findings of AI and digital image analysis studies. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review.
Saxena Shweta,Gyanchandani Manasi
Journal of medical imaging and radiation sciences
Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer using histopathology images of conventional photomicroscopy. Cancer diagnosis is the identification of images as cancer or noncancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. In this article, different approaches to perform these necessary steps are reviewed. We find that most ML research for breast cancer diagnosis has been focused on deep learning. Based on inferences from the recent research activities, we discuss how ML methods can benefit conventional microscopy-based breast cancer diagnosis. Finally, we discuss the research gaps of ML approaches for the implementation in a real pathology environment and propose future research guidelines.
The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.
Sultan Ahmed S,Elgharib Mohamed A,Tavares Tiffany,Jessri Maryam,Basile John R
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND:Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage. DISCUSSION:A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed. CONCLUSION:Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.
Oligodendrogliomas, IDH-mutant and 1p/19q-codeleted, arising during teenage years often lack TERT promoter mutation that is typical of their adult counterparts.
Lee Julieann,Putnam Angelica R,Chesier Samuel H,Banerjee Anuradha,Raffel Corey,Van Ziffle Jessica,Onodera Courtney,Grenert James P,Bastian Boris C,Perry Arie,Solomon David A
Acta neuropathologica communications
Artificial Intelligence Neuropathologist for Glioma Classification using Deep Learning on Hematoxylin and Eosin Stained Slide images and Molecular Markers.
Jin Lei,Shi Feng,Chun Qiuping,Chen Hong,Ma Yixin,Hameed N U Frarrukh,Wu Shuai,Mei Chunming,Lu Junfeng,Zhang Jun,Aibaidula Abudumijiti,Shen Dinggang,Wu Jinsong
BACKGROUND:Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification. METHODS:A neuropathological diagnostic platform is designed comprising of a slide scanner and deep convolutional neural networks (CNNs) to classify five major histological subtypes of glioma to assist pathologists. The CNNs were trained and verified on over 79,990 histological patch images from 267 patients. A logical algorithm is used when molecular profiles are available. RESULTS:A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which learns the recognizable features of glioma histology CNN-based independent diagnostic testing on data from 56 patients with 17,262 histological patch images demonstrated patch level accuracy of 86.5% and patient level accuracy of 87.5%. Histo-patholgical classifications could be further amplified to integrated neuropathological diagnosis by two molecular markers (IDH and 1p/19q). CONCLUSION:The model is capable of solving multiple classification tasks and can satisfactorily able to classify glioma subtypes. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma.