AIMC Topic: Image Processing, Computer-Assisted

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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

The European respiratory journal
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is i...

Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning.

IEEE transactions on medical imaging
Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is diffic...

Comparison between two programs for image analysis, machine learning and subsequent classification.

Tissue & cell
In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cyto...

A data-driven approach to referable diabetic retinopathy detection.

Artificial intelligence in medicine
UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.

Cardiac Rhythm Device Identification Using Neural Networks.

JACC. Clinical electrophysiology
OBJECTIVES: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify the manufacturer and even the model group of a pacemaker or defibrillator from a chest radiograph.

Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control.

NeuroImage
We introduce Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans. Next to the Bayesian fully convolutional neural network, we also present inherent measures of segmentation uncertainty that allow for qualit...

Quality and content analysis of fundus images using deep learning.

Computers in biology and medicine
Automatic retinal image analysis has remained an important topic of research in the last ten years. Various algorithms and methods have been developed for analysing retinal images. The majority of these methods use public retinal image databases for ...

Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.

Sensors (Basel, Switzerland)
The nematode is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and r...

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Scientific reports
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine lea...

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides.

Scientific reports
Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of ren...