A5022125593- AI in cancer detection
- Cell Image Analysis Techniques
- Digital Imaging for Blood Diseases
- Radiomics and Machine Learning in Medical Imaging
- Image Retrieval and Classification Techniques
- Cutaneous Melanoma Detection and Management
- Cervical Cancer and HPV Research
- Cancer Genomics and Diagnostics
- Advanced Image and Video Retrieval Techniques
- Pancreatic and Hepatic Oncology Research
- COVID-19 Clinical Research Studies
- Long-Term Effects of COVID-19
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- COVID-19 and healthcare impacts
- Economic and Financial Impacts of Cancer
- Colorectal Cancer Screening and Detection
Mayo Clinic in Florida
2023-2024
Intel (United States)
2024
Tehran University of Medical Sciences
2022-2023
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" them. Initially, the authors elaborate on these after separating them according their mitigation strategies: those that need innovative approaches, time, or future technological capabilities require conceptual reappraisal from critical perspective. Then, by integrating hidden information extracted ML...
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With increasing availability whole-slide images (WSIs), demand is growing for efficient retrieval, processing, analysis relevant from vast biomedical archives. However, processing WSIs presents challenges due to their large size content complexity. Full computer digestion impractical, all patches individually prohibitively expensive. In this article, we...
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in field medicine, utilizing images from online data sources such as Twitter and PubMed. Foundation are large, deep artificial neural networks capable learning context a specific domain through training exceptionally extensive datasets. Through validation, we observed that representations generated by exhibit inferior performance retrieval tasks within digital pathology when compared to...
Abstract Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence pathological lesions a WSI, confounding role and redundant nature normal histology generally overlooked. In this paper, we propose validate concept “atlas tissue” solely using samples WSIs obtained from biopsies. Such atlases can be employed eliminate fragments tissue hence increase representativeness remaining patches. We tested our...
Searching for similar images in archives of histology and histopathology is a crucial task that may aid patient matching various purposes, ranging from triaging diagnosis to prognosis prediction. Whole slide (WSIs) are highly detailed digital representations tissue specimens mounted on glass slides. Matching WSI can serve as the critical method matching. In this paper, we report extensive analysis validation four search methods bag visual words (BoVW), Yottixel, SISH, RetCCL, some their...
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With increasing availability Whole Slide Images (WSIs), there's a growing demand for efficient retrieval, processing, analysis relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size content complexity. Full computer digestion is impractical, all patches individually prohibitively expensive. In...
In this article, we aimed to report the incidence rate of PC at national and regional levels Iran from 2014 2017 for first time based on IARC protocols.The data was recruited Iranian program cancer registry, a registry reformed in after including diagnosis clinical judgment death certificates. This includes pathology laboratories sectors included with certificates 60 medical universities 31 provinces Iran. Age-standardized rates were calculated levels.From 2017, 8851 new cases (males=60.46%)...
Patching gigapixel whole slide images (WSIs) is an important task in computational pathology. Some methods have been proposed to select a subset of patches as WSI representation for downstream tasks. While most the pathology tasks are designed classify or detect presence pathological lesions each WSI, confounding role and redundant nature normal histology tissue samples generally overlooked representations. In this paper, we propose validate concept "atlas tissue" solely using WSIs obtained...
Abstract Patching whole slide images (WSIs) is an important task in computational pathology. While most of the are designed to classify or detect presence pathological lesions a WSI, confounding role and redundant nature normal histology generally overlooked. In this paper, we propose validate concept “atlas tissue” solely using samples WSIs obtained from biopsies. Such atlases can be employed eliminate fragments tissue hence increase representativeness remaining patches. We tested our...
This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly, it introduces a fast patch selection method, FPS, for whole-slide (WSI) analysis, significantly reducing computational cost while maintaining accuracy. Secondly, presents PathDino, lightweight histopathology feature extractor with minimal configuration of five Transformer blocks and only 9 million parameters, markedly fewer than alternatives. Thirdly, rotation-agnostic...
Whole slide images (WSIs) are massive digital pathology files illustrating intricate tissue structures. Selecting a small, representative subset of patches from each WSI is essential yet challenging. Therefore, following the "Divide & Conquer" approach becomes to facilitate analysis including classification and matching in computational pathology. To this end, we propose novel method termed "Selection Distinct Morphologies" (SDM) choose patches. The aim encompass all inherent morphological...