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

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.

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A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer

Cancers ◽

10.3390/cancers13153825 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3825

Author(s):

Md Mostafa Kamal Sarker ◽

Yasmine Makhlouf ◽

Stephanie G. Craig ◽

Matthew P. Humphries ◽

Maurice Loughrey ◽

...

Keyword(s):

Deep Learning ◽

T Cell ◽

Protein Expression ◽

Open Source ◽

Open Source Software ◽

Digital Pathology ◽

Response To Therapy ◽

Cell Segmentation ◽

Nuclear Staining ◽

Annotation Process

Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.

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A Deep Learning-Aided Decision-Support Tool for Precise Prediction of Lymph Node Metastasis in Patients with Prostate Cancer

SSRN Electronic Journal ◽

10.2139/ssrn.3590447 ◽

2020 ◽

Author(s):

Ying Hou ◽

Yang Song ◽

Mei-Ling Bao ◽

Chen-Jiang Wu ◽

Jing Zhang ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Lymph Node ◽

Decision Support ◽

Lymph Node Metastasis ◽

Decision Support Tool ◽

Support Tool ◽

Node Metastasis ◽

Precise Prediction

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Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Scientific Reports ◽

10.1038/s41598-021-88236-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Christian Crouzet ◽

Gwangjin Jeong ◽

Rachel H. Chae ◽

Krystal T. LoPresti ◽

Cody E. Dunn ◽

...

Keyword(s):

Deep Learning ◽

Prussian Blue ◽

Processing Speed ◽

Digital Pathology ◽

Ground Truth ◽

Individual Variability ◽

Rgb Images ◽

Cerebral Microhemorrhages ◽

Phasor Analysis ◽

Better Than

AbstractCerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

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Quality control stress test for deep learning-based diagnostic model in digital pathology

Modern Pathology ◽

10.1038/s41379-021-00859-x ◽

2021 ◽

Author(s):

Birgid Schömig-Markiefka ◽

Alexey Pryalukhin ◽

Wolfgang Hulla ◽

Andrey Bychkov ◽

Junya Fukuoka ◽

...

Keyword(s):

Deep Learning ◽

Cancer Detection ◽

Stress Testing ◽

Computational Analysis ◽

Stress Test ◽

Digital Pathology ◽

Model Performance ◽

Diagnostic Model ◽

Model Accuracy ◽

Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

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Smartphone-based DNA diagnostics for malaria detection using deep learning for local decision support and blockchain technology for security

Nature Electronics ◽

10.1038/s41928-021-00612-x ◽

2021 ◽

Author(s):

Xin Guo ◽

Muhammad Arslan Khalid ◽

Ivo Domingos ◽

Anna Lito Michala ◽

Moses Adriko ◽

...

Keyword(s):

Deep Learning ◽

Decision Support ◽

Dna Diagnostics ◽

Blockchain Technology ◽

Local Decision

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A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽

10.3390/f12030294 ◽

2021 ◽

Vol 12 (3) ◽

pp. 294

Author(s):

Nicholas F. McCarthy ◽

Ali Tohidi ◽

Yawar Aziz ◽

Matt Dennie ◽

Mario Miguel Valero ◽

...

Keyword(s):

Deep Learning ◽

Decision Support ◽

Real Time ◽

Forest Fires ◽

Wildland Fire ◽

Fire Spread ◽

Low Earth Orbit ◽

Practical Implementation ◽

Spatiotemporal Resolution ◽

Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

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

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.1c00103 ◽

2021 ◽

Author(s):

Harrison Green ◽

Jacob D. Durrant

Keyword(s):

Deep Learning ◽

Open Source ◽

Lead Optimization

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A30 Tumor-Infiltrating Lymphocytes (TILs) Found Elevated in Lung Adenocarcinomas (LUAD) Using Automated Digital Pathology Masks Derived from Deep-Learning Models

Journal of Thoracic Oncology ◽

10.1016/j.jtho.2019.12.059 ◽

2020 ◽

Vol 15 (2) ◽

pp. S22

Author(s):

M.I. Jaber ◽

L. Beziaeva ◽

S.C. Benz ◽

S.K. Reddy ◽

S. Rabizadeh ◽

...

Keyword(s):

Deep Learning ◽

Digital Pathology ◽

Tumor Infiltrating Lymphocytes ◽

Learning Models ◽

Lung Adenocarcinomas ◽

Infiltrating Lymphocytes

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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Journal of Imaging ◽

10.3390/jimaging7030051 ◽

2021 ◽

Vol 7 (3) ◽

pp. 51

Author(s):

Emanuela Paladini ◽

Edoardo Vantaggiato ◽

Fares Bougourzi ◽

Cosimo Distante ◽

Abdenour Hadid ◽

...

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Digital Pathology ◽

Texture Features ◽

Tissue Type ◽

Cancer Tissue ◽

Learning Methods ◽

Feature Based ◽

Type Classification ◽

Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

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