Deep Learning-Based Software for Automated Cell Detection and Counting in Whole-Slide Histological Image Analysis

Edgars Edelmers; Inese Fišere

doi:10.25143/rsu-balt-morf-11-meeting

Deep Learning-Based Software for Automated Cell Detection and Counting in Whole-Slide Histological Image Analysis

Edgars Edelmers, Inese Fišere

Institute of Anatomy and Anthropology

Research output: Contribution to conference › Abstract › peer-review

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Abstract

This study presents the development of a deep learning-based software designed to automate and accelerate routine cell detection, counting, and surface area calculation in whole-slide histological images. The tool aims to improve efficiency and reduce the manual workload involved in analysing such images, which is crucial for both clinical diagnostics and research applications.

Original language	English
Pages	67
DOIs	https://doi.org/10.25143/rsu-balt-morf-11-meeting
Publication status	Published - Nov 2024
Event	11th Baltic Morphology Meeting - Theatrum Anatomicum, Rīga, Latvia Duration: 13 Nov 2024 → 15 Nov 2024 Conference number: 11 https://www.rsu.lv/en/balticmorphology2024

Meeting

Meeting	11th Baltic Morphology Meeting
Country/Territory	Latvia
City	Rīga
Period	13/11/24 → 15/11/24
Internet address	https://www.rsu.lv/en/balticmorphology2024

Keywords*

histology
artificial intelligence
computer vision

Field of Science*

1.2 Computer and information sciences
1.6 Biological sciences
3.1 Basic medicine

Publication Type*

3.4. Other publications in conference proceedings (including local)

Access to Document

10.25143/rsu-balt-morf-11-meetingLicence: CC BY-NC

Deep learning-based software for automated cell detection and counting in whole-slide histological image analysisFinal published version, 57.7 KBLicence: CC BY-NC

Cite this

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title = "Deep Learning-Based Software for Automated Cell Detection and Counting in Whole-Slide Histological Image Analysis",

abstract = "This study presents the development of a deep learning-based software designed to automate and accelerate routine cell detection, counting, and surface area calculation in whole-slide histological images. The tool aims to improve efficiency and reduce the manual workload involved in analysing such images, which is crucial for both clinical diagnostics and research applications.",

keywords = "histology, artificial intelligence, computer vision",

author = "Edgars Edelmers and Inese Fi{\v s}ere",

year = "2024",

month = nov,

doi = "10.25143/rsu-balt-morf-11-meeting",

language = "English",

pages = "67",

note = "11th Baltic Morphology Meeting ; Conference date: 13-11-2024 Through 15-11-2024",

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T1 - Deep Learning-Based Software for Automated Cell Detection and Counting in Whole-Slide Histological Image Analysis

AU - Edelmers, Edgars

AU - Fišere, Inese

N1 - Conference code: 11

PY - 2024/11

Y1 - 2024/11

N2 - This study presents the development of a deep learning-based software designed to automate and accelerate routine cell detection, counting, and surface area calculation in whole-slide histological images. The tool aims to improve efficiency and reduce the manual workload involved in analysing such images, which is crucial for both clinical diagnostics and research applications.

AB - This study presents the development of a deep learning-based software designed to automate and accelerate routine cell detection, counting, and surface area calculation in whole-slide histological images. The tool aims to improve efficiency and reduce the manual workload involved in analysing such images, which is crucial for both clinical diagnostics and research applications.

KW - histology

KW - artificial intelligence

KW - computer vision

UR - https://www.rsu.lv/en/balticmorphology2024

U2 - 10.25143/rsu-balt-morf-11-meeting

DO - 10.25143/rsu-balt-morf-11-meeting

M3 - Abstract

SP - 67

T2 - 11th Baltic Morphology Meeting

Y2 - 13 November 2024 through 15 November 2024

ER -

Deep Learning-Based Software for Automated Cell Detection and Counting in Whole-Slide Histological Image Analysis

Abstract

Meeting

Keywords*

Field of Science*

Publication Type*

Access to Document

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