A survey on recent trends in deep learning for nucleus segmentation from histopathology images
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Mitosis domain generalization in histopathology images—the MIDOG challenge
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
Unleashing the potential of AI for pathology: challenges and recommendations
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
Deep interactive segmentation of medical images: A systematic review and taxonomy
Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …
boost the efficiency of costly annotations by incorporating human feedback. This feedback …
[HTML][HTML] Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
N Zamanitajeddin, M Jahanifar, M Bilal… - Medical Image …, 2024 - Elsevier
Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular
pathways, genetic subtypes, and mutations associated with CRC is crucial for precision …
pathways, genetic subtypes, and mutations associated with CRC is crucial for precision …
[HTML][HTML] Domain generalization across tumor types, laboratories, and species—Insights from the 2022 edition of the Mitosis Domain Generalization Challenge
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient
outcome assessment. This task is challenging for algorithms and human experts alike, with …
outcome assessment. This task is challenging for algorithms and human experts alike, with …
Wsss4luad: Grand challenge on weakly-supervised tissue semantic segmentation for lung adenocarcinoma
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD)
is the most common subtype. Exploiting the potential value of the histopathology images can …
is the most common subtype. Exploiting the potential value of the histopathology images can …
Stain-robust mitotic figure detection for the mitosis domain generalization challenge
M Jahanifar, A Shepard, N Zamanitajeddin… - … Conference on Medical …, 2021 - Springer
The detection of mitotic figures from different scanners/sites remains an important topic of
research, owing to its potential in assisting clinicians with tumour grading. The MItosis …
research, owing to its potential in assisting clinicians with tumour grading. The MItosis …
[HTML][HTML] Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey
Digital pathology technologies, including whole slide imaging (WSI), have significantly
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …
improved modern clinical practices by facilitating storing, viewing, processing, and sharing …