Digital pathology 2.0: a deep learning image analysis tool to identify lung cancer in human tissue samples

2018 ◽  
Author(s):  
Patrick Micke
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Human Tissue ◽  
Digital Pathology ◽  
Analysis Tool ◽  
Tissue Samples

scholarly journals Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1673 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Junya Fujimoto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Diagnosis And Prognosis ◽  
Model Interpretation ◽  
Lung Cancer Diagnosis ◽  
Cancer Pathology ◽  
Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

scholarly journals An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chi-Long Chen ◽  
Chi-Chung Chen ◽  
Wei-Hsiang Yu ◽  
Szu-Hua Chen ◽  
Yu-Chan Chang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Digital Pathology ◽  
Classification Performance ◽  
Multiple Instance Learning ◽  
Data Set ◽  
Memory Mechanism ◽  
Strong Localization ◽  
Training Approach

AbstractDeep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

Deep learning in digital pathology image analysis: a survey

2020 ◽  
Vol 14 (4) ◽  
pp. 470-487
Author(s):  
Shujian Deng ◽  
Xin Zhang ◽  
Wen Yan ◽  
Eric I-Chao Chang ◽  
Yubo Fan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Pathology Image

scholarly journals Developing image analysis pipelines of whole-slide images: Pre- and post-processing

2020 ◽  
pp. 1-11
Author(s):  
Byron Smith ◽  
Meyke Hermsen ◽  
Elizabeth Lesser ◽  
Deepak Ravichandar ◽  
Walter Kremers
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Digital Pathology ◽  
Disruptive Technology ◽  
Post Processing ◽  
Artifact Detection ◽  
The Press ◽  
Slide Image ◽  
Processing Order ◽  
Whole Slide Images

Abstract Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size – typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.

scholarly journals Deep learning method for comet segmentation and comet assay image analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yiyu Hong ◽  
Hyo-Jeong Han ◽  
Hannah Lee ◽  
Donghwan Lee ◽  
Junsu Ko ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Comet Assay ◽  
Tail Length ◽  
High Sensitivity ◽  
Analysis Tool ◽  
Test Results ◽  
Practical Application ◽  
Average Precision ◽  
The Arts

Abstract Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

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