AIMC Topic: Histological Techniques

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Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

The Lancet. Digital health Oct 19, 2021
BACKGROUND: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pa...
Biomarkers, Tumor Mutation Humans Colon Rectum Deep Learning Histological Techniques Phenotype ROC Curve Retrospective Studies Area Under Curve Colorectal Neoplasms Microsatellite Instability
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Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology.

IEEE transactions on visualization and computer graphics Sep 1, 2021
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and patholog...
Breast Neoplasms Radiography Image Interpretation, Computer-Assisted Histological Techniques Humans Imaging Genomics Machine Learning Algorithms Female
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Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.

The Lancet. Digital health Aug 17, 2021
BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers ...
Deep Learning Cohort Studies United States Stomach Neoplasms Male Middle Aged Italy Japan United Kingdom Epstein-Barr Virus Infections Switzerland Microsatellite Instability Humans Retrospective Studies Republic of Korea Female Reproducibility of Results Aged Germany Histological Techniques
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Convolutional neural networks for cytoarchitectonic brain mapping at large scale.

NeuroImage Jul 2, 2021
Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional di...
Humans Magnetic Resonance Imaging Neural Networks, Computer Brain Mapping Databases, Factual Brain Histological Techniques Deep Learning Imaging, Three-Dimensional
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Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.

Scientific reports Jul 1, 2021
Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultim...
Microscopy Image Interpretation, Computer-Assisted Histological Techniques Rectal Neoplasms Heart Failure Humans Neural Networks, Computer Artificial Intelligence Colorectal Neoplasms Diagnosis, Computer-Assisted Image Processing, Computer-Assisted
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SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images.

Medical image analysis Jun 21, 2021
The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional ob...
Image Processing, Computer-Assisted Cell Nucleus Histological Techniques Neural Networks, Computer
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Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images.

Medical image analysis May 24, 2021
Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treat...
Humans Cell Nucleus Neural Networks, Computer Histological Techniques Algorithms Prognosis
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Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

IEEE journal of biomedical and health informatics May 11, 2021
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the...
Cell Nucleus Histological Techniques Neural Networks, Computer Humans Deep Learning Image Processing, Computer-Assisted
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HistoNet: A Deep Learning-Based Model of Normal Histology.

Toxicologic pathology Mar 3, 2021
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From the...
Histological Techniques Swine, Miniature Swine Humans Deep Learning Neural Networks, Computer Rats Machine Learning Animals
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Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology.

Toxicologic pathology Feb 24, 2021
Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be di...
Pathologists Biomarkers Humans Histological Techniques Animals Machine Learning Rats
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