AIMC Topic: Histological Techniques
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Interactive phenotyping of large-scale histology imaging data with HistomicsML.
Scientific reports
Nov 6, 2017
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundre...
Cervical cancer histology image identification method based on texture and lesion area features.
Computer assisted surgery (Abingdon, England)
Oct 16, 2017
The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise o...
A structured latent model for ovarian carcinoma subtyping from histopathology slides.
Medical image analysis
May 9, 2017
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel...
DCAN: Deep contour-aware networks for object instance segmentation from histology images.
Medical image analysis
Nov 16, 2016
In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these obj...
When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.
Medical image analysis
Sep 9, 2016
Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models...
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.
Scientific reports
May 23, 2016
Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we int...
Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 2, 2014
This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving ...
Pattern Recognition, Automated
Cell Nucleus
Image Enhancement
Microscopy
Image Interpretation, Computer-Assisted
Reproducibility of Results
Semantics
Sensitivity and Specificity
Breast Neoplasms
Mitosis
User-Computer Interface
Histological Techniques
Neoplasm Grading
Signal Processing, Computer-Assisted
Software
Humans
Algorithms
Machine Learning
Female
DEPAS: De-novo Pathology Semantic Masks using a Generative Model.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
The integration of artificial intelligence (AI) into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need fo...
A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specific...
Deep learning links histology, molecular signatures and prognosis in cancer.
Nature cancer
Aug 1, 2020
Deep learning can be used to predict genomic alterations based on morphological features learned from digital histopathology. Two independent pan-cancer studies now show that automated learning from digital pathology slides and genomics can potential...
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