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 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 Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

2020 ◽  
pp. 221-233
Author(s):  
Yijiang Chen ◽  
Andrew Janowczyk ◽  
Anant Madabhushi
Keyword(s):  
Deep Learning ◽  
Image Compression ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Characteristic Curve ◽  
Digital Pathology ◽  
Jpeg2000 Compression ◽  
Classifier Performance ◽  
Pathology Image

PURPOSE Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored. METHODS We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.

scholarly journals Pathology Image Analysis Using Segmentation Deep Learning Algorithms

2019 ◽  
Vol 189 (9) ◽  
pp. 1686-1698 ◽  
Author(s):  
Shidan Wang ◽  
Donghan M. Yang ◽  
Ruichen Rong ◽  
Xiaowei Zhan ◽  
Guanghua Xiao
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Pathology Image

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

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.

Sign in / Sign up

Export Citation Format

Share Document