Contact us
|
Home
|
Login
| Users Online: 274
Feedback
Subscribe
Advertise
Search
Advanced Search
Month wise articles
Figures next to the month indicate the number of articles in that month
2021
February
[
3
]
January
[
3
]
2020
December
[
1
]
November
[
1
]
October
[
2
]
September
[
1
]
August
[
4
]
July
[
1
]
April
[
1
]
March
[
1
]
February
[
4
]
2019
December
[
2
]
September
[
2
]
July
[
2
]
April
[
1
]
February
[
1
]
2018
December
[
4
]
November
[
1
]
October
[
3
]
September
[
1
]
July
[
1
]
May
[
1
]
April
[
2
]
March
[
1
]
February
[
2
]
2017
December
[
3
]
March
[
3
]
2016
January
[
1
]
2014
September
[
1
]
» Articles published in the past year
To view other articles click corresponding year from the navigation links on the left side.
All
|
Abstracts
|
Commentary
|
Editorial
|
Erratum
|
Original Article
|
Original Articles
|
Original Research
|
Research Article
|
Review Article
|
Technical Note
Export selected to
Endnote
Reference Manager
Procite
Medlars Format
RefWorks Format
BibTex Format
Show all abstracts
Show selected abstracts
Export selected to
Add to my list
Original Article:
Computational algorithms that effectively reduce report defects in surgical pathology
Jay J Ye, Michael R Tan
J Pathol Inform
2019, 10:20 (1 July 2019)
DOI
:10.4103/jpi.jpi_17_19
PMID
:31367472
Background:
Pathology report defects refer to errors in the pathology reports, such as transcription/voice recognition errors and incorrect nondiagnostic information. Examples of the latter include incorrect gender, incorrect submitting physician, incorrect description of tissue blocks submitted, report formatting issues, and so on. Over the past 5 years, we have implemented computational algorithms to identify and correct these report defects.
Materials and Methods:
Report texts, tissue blocks submitted, and other relevant information are retrieved from the pathology information system database. Two complementary algorithms are used to identify the voice recognition errors by parsing the gross description texts to either (i) identify previously encountered error patterns or (ii) flag sentences containing previously-unused two-word sequences (bigrams). A third algorithm based on identifying conflicting information from two different sources is used to identify tissue block designation errors in the gross description; the information on actual block submission is compared with the block designation information parsed from the gross description text.
Results:
The computational algorithms identify voice recognition errors in approximately 8%–10% of the cases and block designation errors in approximately 0.5%–1% of all the cases.
Conclusions:
The algorithms described here have been effective in reducing pathology report defects. In addition to detecting voice recognition and block designation errors, these algorithms have also be used to detect other report defects, such as wrong gender, wrong provider, special stains or immunostains performed but not reported, and so on.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Original Article:
Deep learning-based retrieval system for gigapixel histopathology cases and the open access literature
Roger Schaer, Sebastian Otálora, Oscar Jimenez-del-Toro, Manfredo Atzori, Henning Müller
J Pathol Inform
2019, 10:19 (1 July 2019)
DOI
:10.4103/jpi.jpi_88_18
PMID
:31367471
Background:
The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists.
Objectives:
The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text.
Methods:
An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features.
Results:
The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice.
Conclusions:
The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Sitemap
|
What's New
|
Feedback
|
Disclaimer
|
© Journal of Pathology Informatics | Published by Wolters Kluwer -
Medknow
Online since 10
th
March, 2010