scholarly journals Deep Learning Based Retrieval System for Gigapixel Histopathology Cases and Open Access Literature

2018 ◽  
Author(s):  
Sebastian Otálora ◽  
Roger Schaer ◽  
Oscar Jimenez-del-Toro ◽  
Manfredo Atzori ◽  
Henning Müller
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Retrieval System ◽  
Digital Pathology ◽  
Image Features ◽  
Retrieval Performance ◽  
Slide Image ◽  
Text Information ◽  
Computation Algorithms ◽  
Whole Slide Images

ABSTRACTClinical practice is getting increasingly stressful for pathologists due to increasing complexity and time constraints. Histopathology is slowly shifting to digital pathology, thus creating opportunities to allow pathologists to improve reading quality or save time using Artificial Intelligence (AI)-based applications. We aim to enhance the practice of pathologists through a retrieval system that allows them to simplify their workflow, limit the need for second opinions, while also learning in the process. In this work, an innovative retrieval system for digital pathology is integrated within a Whole Slide Image (WSI) viewer, allowing to define regions of interest in images as queries for finding visually similar areas using deep representations. The back-end similarity computation algorithms are based on a multimodal approach, allowing to exploit both text information and content-based image features. Shallow and deep representations of the images were evaluated, the later showed a better overall retrieval performance in a set of 112 whole slide images from biopsies. The system was also tested by pathologists, highlighting its capabilities and suggesting possible ways to improve it and make it more usable in clinical practice. The retrieval system developed can enhance the practice of pathologists by enabling them to use their experience and knowledge to properly control artificial intelligence tools for navigating repositories of images for decision support purposes.

scholarly journals Content based Image Retrieval System using Combination of Color and Shape Features, and Siamese Neural Network

2019 ◽  
Vol 9 (2S) ◽  
pp. 71-77
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Retrieval System ◽  
Image Features ◽  
Shape Features ◽  
Retrieval Performance ◽  
Novel Approach ◽  
Learning Technique ◽  
Image Retrieval System ◽  
Similar Images

With an advent of technologya huge collection of digital images is formed as repositories on world wide web (WWW). The task of searching for similar images in the repository is difficult. In this paper, retrieval of similar images from www is demonstrated with the help of combination of image features as color and shape and then using Siamese neural network which is constructed to the requirement as a novel approach. Here, one-shot learning technique is used to test the Siamese Neural Network model for retrieval performance. Various experiments are conducted with both the methods and results obtained are tabulated. The performance of the system is evaluated with precision parameter and which is found to be high.Also, relative study is made with existing works.

Figure Based Biomedical Document Retrieval System using Structural Image Features

2012 ◽  
Vol 3 (1) ◽  
pp. 39-58
Author(s):  
Harikrishna G. N. Rai ◽  
K Sai Deepak ◽  
P. Radha Krishna
Keyword(s):  
Structural Properties ◽  
Retrieval System ◽  
Document Retrieval ◽  
Image Features ◽  
Biomedical Literature ◽  
Feature Descriptor ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
Edge Information ◽  
Structural Image

Multi-modal and Unstructured nature of documents make their retrieval from healthcare document repositories a challenging task. Text based retrieval is the conventional approach used for solving this problem. In this paper, the authors explore an alternate avenue of using embedded figures for the retrieval task. Usually, context of a document is directly reflected in the associated figures, therefore embedded text within these figures along with image features have been used for similarity based retrieval of figures. The present work demonstrates that image features describing the structural properties of figures are sufficient for the figure retrieval task. First, the authors analyze the problem of figure retrieval from biomedical literature and identify significant classes of figures. Second, they use edge information as a means to discriminate between structural properties of each figure category. Finally, the authors present a methodology using a novel feature descriptor namely Fourier Edge Orientation Autocorrelogram (FEOAC) to describe structural properties of figures and build an effective Biomedical document retrieval system. The experimental results demonstrate the better retrieval performance and overall improvement of FEOAC for figure retrieval task, especially when most of the edge information is retained. Apart from invariance to scale, rotation and non-uniform illumination, the proposed feature descriptor is shown to be relatively robust to noisy edges.

Dawn of artificial intelligence -Enable digital Pathology in Pakistan-A paradigm shift

2021 ◽  
pp. 1-5
Author(s):  
Talat Zehra ◽  
Asma Shaikh ◽  
Maheen Shams
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Digital Pathology ◽  
Whole Slide Imaging ◽  
Storage Devices ◽  
New Horizons ◽  
Novel Technology ◽  
The World ◽  
Slide Image ◽  
Computational Pathology

Pathology particularly histopathology is considered to be a busy and challenging field. It is considered as gold standard for the diagnosis and management of patient particularly in cases of tumor. It has been more than twenty years since the introduction of whole slide imaging (WSI) in the developed part of the world. Various whole slide image (WSI) devices and use of artificial intelligence (AI) based softwares have transformed the field of Pathology1. Digital pathology is a novel technology and currently being implemented in most of the developed part of the world.2 Once the patient’s data becomes digital, it is easily stored, reproducible on a single click and quality remains same. This data can be used to make disease models, disease trends and predict the outcome of a particular disease through data mining which will open new horizons of precise medicine. The use of WSI with computational pathology and data storage devices have revolutionized the working in histopathology. The world witnessed an exponential rise in its adoption particularly after Covid-19 pandemic1. However, in the developing world either it is not being implemented or its use is still sub-optimal. By realizing the potential of digital and computational pathology along with the use of artificial intelligence software, we can bring a drastic change in the field of personalized medicine in the developing part of the world 3. Numerous validation studies have been published indicating that WSI is a reliable tool for routine diagnosis in surgical pathology 4   Continuous...

scholarly journals Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors

2020 ◽  
Vol 144 (11) ◽  
pp. 1397-1400 ◽  
Author(s):  
Zi Long Chow ◽  
Aye Aye Thike ◽  
Hui Hua Li ◽  
Nur Diyana Md Nasir ◽  
Joe Poh Sheng Yeong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Correlation Coefficients ◽  
Routine Practice ◽  
Phyllodes Tumors ◽  
Slide Image ◽  
Clinicopathologic Parameters ◽  
Histologic Criterion ◽  
Mitotic Counting ◽  
Tumor Area

Context.— Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification. Objective.— To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis. Design.— Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted. Results.— Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size. Conclusions.— Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.

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 Digital Pathology: Advantages, Limitations and Emerging Perspectives

2020 ◽  
Vol 9 (11) ◽  
pp. 3697
Author(s):  
Stephan W. Jahn ◽  
Markus Plass ◽  
Farid Moinfar
Keyword(s):  
Artificial Intelligence ◽  
Occupational Health ◽  
Large Scale ◽  
Digital Pathology ◽  
Health Issues ◽  
Routine Diagnostics ◽  
Starting Point ◽  
Slide Image ◽  
Traditional Work ◽  
Image Archiving

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist’s profession.

scholarly journals Robust Hierarchical Density Estimation and Regression for Re-stained Histological Whole Slide Image Co-registration

2019 ◽  
Author(s):  
Jun Jiang ◽  
Nicholas B. Larson ◽  
Naresh Prodduturi ◽  
Thomas J. Flotte ◽  
Steven N. Hart
Keyword(s):  
Image Registration ◽  
Density Estimation ◽  
Digital Pathology ◽  
Tissue Samples ◽  
Registration Errors ◽  
Slide Image ◽  
Specific Proteins ◽  
Histological Images ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images

AbstractFor many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs – particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when on matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.

scholarly journals Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 797 ◽  
Author(s):  
Hanadi El El Achi ◽  
Joseph D. Khoury
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Digital Images ◽  
Digital Pathology ◽  
Digital Microscopy ◽  
Clinical Environment ◽  
Lymphoproliferative Diseases ◽  
Glass Slides ◽  
Pathology Data ◽  
Whole Slide Images

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

scholarly journals Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

2019 ◽  
Vol 249 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Richard Colling ◽  
Helen Pitman ◽  
Karin Oien ◽  
Nasir Rajpoot ◽  
Philip Macklin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Digital Pathology

scholarly journals Artificial Intelligence (AI) in Pathology – A Summary and Challenges

2021 ◽  
pp. 23-34
Author(s):  
Archana Buch ◽  
Rohan Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Data Quality ◽  
Digital Pathology ◽  
Regulatory Approval ◽  
Cost Data ◽  
Unsupervised Machine Learning ◽  
Implementation Challenges ◽  
Bibliographic Study

This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

Sign in / Sign up

Export Citation Format

Share Document