[HTML][HTML] All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems
Abstract Background and Objectives Artificial intelligence (AI) models trained on multi-
centric and multi-device studies can provide more robust insights and research findings …
centric and multi-device studies can provide more robust insights and research findings …
Artificial intelligence applications in histopathology
Histopathology is a vital diagnostic discipline in medicine, fundamental to our
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
[HTML][HTML] BCD-net: Stain separation of histological images using deep variational Bayesian blind color deconvolution
Histological images are often tainted with two or more stains to reveal their underlying
structures. Blind Color Deconvolution (BCD) techniques separate colors (stains) and …
structures. Blind Color Deconvolution (BCD) techniques separate colors (stains) and …
StainSWIN: Vision transformer-based stain normalization for histopathology image analysis
Stain normalization is a key preprocessing step that has been shown to significantly improve
the segmentation and classification performance of computer-aided diagnosis (CAD) …
the segmentation and classification performance of computer-aided diagnosis (CAD) …
Deep learning for liver cancer histopathology image analysis: A comprehensive survey
Liver cancer is the predominant cause of cancer-related fatalities globally, wherein
Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) emerge as …
Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) emerge as …
Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images
C Franchet, R Schwob, G Bataillon, C Syrykh… - Computers in Biology …, 2024 - Elsevier
Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However,
a frequent drawback of AI models is their propension to make decisions based rather on …
a frequent drawback of AI models is their propension to make decisions based rather on …
RandStainNA++: Enhance Random Stain Augmentation and Normalization through Foreground and Background Differentiation
C Wang, S Li, J Ke, C Zhang… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
The wide prevalence of staining variations in digital pathology presents a significant
obstacle, often undermining the effectiveness of diagnosis and analysis. The current …
obstacle, often undermining the effectiveness of diagnosis and analysis. The current …
CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models
F Fan, G Martinez, T DeSilvio, J Shin, Y Chen, J Jacobs… - npj Imaging, 2024 - nature.com
Batch effects (BEs) refer to systematic technical differences in data collection unrelated to
biological variations whose noise is shown to negatively impact machine learning (ML) …
biological variations whose noise is shown to negatively impact machine learning (ML) …
Softmax-Driven Active Shape Model for Segmenting Crowded Objects in Digital Pathology Images
Automated segmentation of histological structures in microscopy images is a crucial step in
computer-aided diagnosis framework. However, this task remains a challenging problem …
computer-aided diagnosis framework. However, this task remains a challenging problem …
Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques
Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on
the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools …
the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools …