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AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
In this white paper, experts from the Digital Pathology Association (DPA) define terminology
and concepts in the emerging field of computational pathology, with a focus on its …
and concepts in the emerging field of computational pathology, with a focus on its …
A multi-organ nucleus segmentation challenge
Generalized nucleus segmentation techniques can contribute greatly to reducing the time to
develop and validate visual biomarkers for new digital pathology datasets. We summarize …
develop and validate visual biomarkers for new digital pathology datasets. We summarize …
MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge
Detecting various types of cells in and around the tumor matrix holds a special significance
in characterizing the tumor micro-environment for cancer prognostication and research …
in characterizing the tumor micro-environment for cancer prognostication and research …
A dataset and a technique for generalized nuclear segmentation for computational pathology
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-
quality features for nuclear morphometrics and other analysis in computational pathology …
quality features for nuclear morphometrics and other analysis in computational pathology …
Segmentation of nuclei in histopathology images by deep regression of the distance map
The advent of digital pathology provides us with the challenging opportunity to automatically
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
Deep adversarial training for multi-organ nuclei segmentation in histopathology images
Nuclei mymargin segmentation is a fundamental task for various computational pathology
applications including nuclei morphology analysis, cell type classification, and cancer …
applications including nuclei morphology analysis, cell type classification, and cancer …
Structured crowdsourcing enables convolutional segmentation of histology images
Motivation While deep-learning algorithms have demonstrated outstanding performance in
semantic image segmentation tasks, large annotation datasets are needed to create …
semantic image segmentation tasks, large annotation datasets are needed to create …
Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to
extract rich features for cellular estimation and following diagnosis as well as treatment …
extract rich features for cellular estimation and following diagnosis as well as treatment …
Nuclei segmentation using attention aware and adversarial networks
E Goceri - Neurocomputing, 2024 - Elsevier
Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …