scholarly journals HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

2021 ◽  
Author(s):  
Kristopher D McCombe ◽  
Stephanie G Craig ◽  
Amélie Viratham Pulsawatdi ◽  
Javier I Quezada-Marín ◽  
Matthew Hagan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Digital Pathology ◽  
Region Of Interest ◽  
Histological Image ◽  
Cancer Prediction ◽  
New Research ◽  
User Friendly ◽  
Deep Learning Model ◽  
Friendly Graphical User Interface

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

scholarly journals DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
Jacob D. Durrant
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Early Stage ◽  
Molecular Structures ◽  
Lead Optimization ◽  
Third Party ◽  
Link Type ◽  
Version 2.0 ◽  
User Friendly ◽  
Deep Learning Model

1AbstractLead optimization, a critical step in early-stage drug discovery, involves making chemical modifications to a small-molecule ligand to improve its drug-like properties (e.g., binding affinity). We recently developed DeepFrag, a deep-learning model capable of recommending such modifications. Though a powerful hypothesis-generating tool, DeepFrag is currently implemented in Python and so requires a certain degree of computational expertise. To encourage broader adoption, we have created the DeepFrag browser app, which provides a user-friendly graphical user interface that runs the DeepFrag model in users’ web browsers. The browser app does not require users to upload their molecular structures to a third-party server, nor does it require the separate installation of any third-party software. We are hopeful that the app will be a useful tool for both researchers and students. It can be accessed free of charge, without requiring registration, at http://durrantlab.com/deepfrag. The source code is also available at http://git.durrantlab.com/jdurrant/deepfrag-app, released under the terms of the open-source Apache License, Version 2.0.

scholarly journals Deep Learning to Estimate Cardiac Magnetic Resonance-Derived Left Ventricular Mass

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Samuel Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Open Source ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Learning Model ◽  
Left Ventricular ◽  
Lv Mass ◽  
Deep Learning Model

ABSTRACTBackgroundCardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (e.g., inlineVF), but their accuracy and availability may be limited.ObjectiveTo develop an open-source deep learning model to estimate CMR-derived LV mass.MethodsWithin participants of the UK Biobank prospective cohort undergoing CMR, we trained two convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5,065 individuals), while the second (ML4Hseg) performed LV segmentation informed by inlineVF contours. We compared ML4Hreg, ML4Hseg, and inlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex.ResultsWe generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r=0.864, 95% CI 0.847-0.880; MAE 10.41g, 95% CI 9.82-10.99) than ML4Hreg (r=0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, p=0.01) and inlineVF (r=0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, p<0.01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.53, 95% CI 3.16-6.33).ConclusionsML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

scholarly journals FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Andre Pedersen ◽  
Marit Valla ◽  
Anna M. Bofin ◽  
Javier Perez De Frutos ◽  
Ingerid Reinertsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Open Source ◽  
Digital Pathology

scholarly journals Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 253
Author(s):  
Zoltan Czako ◽  
Teodora Surdea-Blaga ◽  
Gheorghe Sebestyen ◽  
Anca Hangan ◽  
Dan Lucian Dumitrascu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Resolution ◽  
Esophageal Manometry ◽  
Region Of Interest ◽  
Failure Detection ◽  
Final Diagnosis ◽  
Exact Moment ◽  
Integrated Relaxation Pressure ◽  
Deep Learning Model

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

scholarly journals RadioPharmaDose, a java-based open-source software for estimating and reporting internal radiation doses

2018 ◽  
Vol 16 (1/2) ◽  
pp. 259-266
Author(s):  
Jaafar EL Bakkali ◽  
Hamid Mansouri ◽  
Abderrahim Doudouh
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Radiation Dosimetry ◽  
Absorbed Dose ◽  
Radiological Protection ◽  
Internal Radiation ◽  
Dosimetric Data ◽  
Sqlite Database ◽  
User Friendly ◽  
Friendly Graphical User Interface

In this work, a user-friendly Java-based open-source software has been developed for internal radiation dosimetry. Based on values published by the International Commission on Radiological Protection (ICRP), the software calculates the estimated absorbed dose for each organ and also the estimated effective dose, this for about forty of the most known radioactive drugs. In addition, the present software offers many features which include: 1) a very friendly graphical user-interface (GUI) designed to facilitate the process of selecting mandatory input data such as radiopharmaceutical product, administered activity and patient's data, 2) a tool for generating a medical report, which can be exported as PDF file or printed directly and then incorporated into the patient's record, 3) a SQLite database for storing patient's specific and dosimetric data. We believe that the present software can be a useful tool for nuclear medicine workers. It is freely available for download on GitHub (https://github.com/EL-Bakkali-Jaafar/RadioPharmaDose).

scholarly journals Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology

2020 ◽  
Vol 10 (18) ◽  
pp. 6179
Author(s):  
Seong Jae Lee ◽  
Joo Young Ko ◽  
Hyun Il Kim ◽  
Sang-Il Choi
Keyword(s):  
Deep Learning ◽  
Present Model ◽  
Spinal Column ◽  
Region Of Interest ◽  
Learning Technology ◽  
Accurate Analysis ◽  
Human Errors ◽  
Airway Invasion ◽  
Human Effort ◽  
Deep Learning Model

In dysphagia, food materials frequently invade the laryngeal airway, potentially resulting in serious consequences, such as asphyxia or pneumonia. The VFSS (videofluoroscopic swallowing study) procedure can be used to visualize the occurrence of airway invasion, but its reliability is limited by human errors and fatigue. Deep learning technology may improve the efficiency and reliability of VFSS analysis by reducing the human effort required. A deep learning model has been developed that can detect airway invasion from VFSS images in a fully automated manner. The model consists of three phases: (1) image normalization, (2) dynamic ROI (region of interest) determination, and (3) airway invasion detection. Noise induced by movement and learning from unintended areas is minimized by defining a “dynamic” ROI with respect to the center of the cervical spinal column as segmented using U-Net. An Xception module, trained on a dataset consisting of 267,748 image frames obtained from 319 VFSS video files, is used for the detection of airway invasion. The present model shows an overall accuracy of 97.2% in classifying image frames and 93.2% in classifying video files. It is anticipated that the present model will enable more accurate analysis of VFSS data.

scholarly journals QuPath: Open source software for digital pathology image analysis

2017 ◽  
Author(s):  
Peter Bankhead ◽  
Maurice B Loughrey ◽  
José A Fernández ◽  
Yvonne Dombrowski ◽  
Darragh G McArt ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Digital Pathology ◽  
Tumor Identification ◽  
Wide Range ◽  
Slide Image ◽  
Bioimage Analysis ◽  
Additional Image ◽  
User Friendly ◽  
Biomarker Evaluation

AbstractQuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA

2021 ◽  
Author(s):  
Te Pei ◽  
Savinay Nagendra ◽  
Srikanth Banagere Manjunatha ◽  
Guanlin He ◽  
Daniel Kifer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
High Rate ◽  
Visual Interpretation ◽  
Web Portal ◽  
Object Based ◽  
Multi Temporal ◽  
Landslide Mapping ◽  
User Friendly ◽  
Deep Learning Model

&lt;p&gt;Landslides are common natural disasters around the globe. Understanding the accurate spatial distribution of landslides is essential for landslide analysis, prediction, and hazard mitigation. So far, many techniques have been used for landslide mapping to establish landslide inventories. However, these techniques either have a low automation level (e.g., visual interpretation-based methods) or a low generalization ability (e.g., pixel-based or object-based approaches); and improvements are required for landslide mapping. Therefore, we have developed an interactive, user-friendly web portal for landslide labeling. The web portal takes multi-temporal satellite images as inputs. A deep learning model will first detect landslide-suspicious areas in the image and present results to users for validation. Users can then review and annotate these machine-labeled landslides through a user-friendly interface. Users&amp;#8217; editions on landslide annotation will further improve the accuracy of the deep learning model. Two landslide-affected regions in Washington were selected to test the capability of our web portal for landslide mapping. The detected landslides were validated by expert labelers. The results indicated that our annotation tool was able to produce landslide maps with high precision, a high rate of annotation, and reduced human efforts.&lt;/p&gt;

scholarly journals Transcriptomic learning for digital pathology

2019 ◽  
Author(s):  
Benoît Schmauch ◽  
Alberto Romagnoni ◽  
Elodie Pronier ◽  
Charlie Saillard ◽  
Pascale Maillé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Digital Pathology ◽  
Gene Mutations ◽  
Predictive Performance ◽  
Molecular Features ◽  
Multiple Data ◽  
Novel Approach ◽  
Expert Annotation ◽  
Deep Learning Model

Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and even to prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that our deep learning model, HE2RNA, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. HE2RNA is interpretable by design, opening up new opportunities for virtual staining. In fact, it provides virtual spatialization of gene expression, as validated by double-staining on an independent dataset. Moreover, the transcriptomic representation learned by HE2RNA can be transferred to improve predictive performance for other tasks, particularly for small datasets. As an example of a task with direct clinical impact, we studied the prediction of microsatellite instability from hematoxylin & eosin stained images and our results show that better performance can be achieved in this setting.

scholarly journals GeoEasy an open source project for surveying calculations

2018 ◽  
Vol 17 (2) ◽  
pp. 1-8 ◽  
Author(s):  
Zoltan Siki
Keyword(s):  
Operating System ◽  
User Interface ◽  
Open Source ◽  
Operating Systems ◽  
Graphical User Interface ◽  
Free Software ◽  
Script Language ◽  
The Core ◽  
User Friendly ◽  
Friendly Graphical User Interface

The development of the GeoEasy program started in 1997. Twenty years later in 2017 it became free software under GPL license, version 3.0.0 is freely available for everybody. The core development of GeoEasy is made on Linux operating system, using Tcl/Tk script language, thanks to the Tcl/Tk ports to other operating systems, the program can be run on Linux, Windows, Android and OSX machines. Objectives of the development are to create user friendly graphical user interface (GUI) for surveying calculations in a modular structure with flexible, open connections to other programs. Both educational and professional usages are supported.

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