Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods

AbstractTissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we investigate different ensemble methods for colorectal tissue classification using high-throughput TMAs. Hematoxylin and Eosin (H&E) core images of 0.6 mm or 1.0 mm diameter from three international cohorts were extracted from 54 digital slides (n = 15,150 cores). After TMA core extraction and color enhancement, five different flows of independent and ensemble deep learning were applied. Training and testing data with 2144 and 13,006 cores included three classes: tumor, normal or “other” tissue. Ground-truth data were collected from 30 ngTMA slides (n = 8689 cores). A test augmentation is applied to reduce the uncertain prediction. Predictive accuracy of the best method, namely Soft Voting Ensemble of one VGG and one CapsNet models was 0.982, 0.947 and 0.939 for normal, “other” and tumor, which outperformed to independent or ensemble learning with one base-estimator. Our high-accuracy algorithm for colorectal tissue classification in high-throughput TMAs is amenable to images from different institutions, core sizes and stain intensity. It helps to reduce error in TMA core evaluations with previously given labels.

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Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽

10.3390/a14070212 ◽

2021 ◽

Vol 14 (7) ◽

pp. 212

Author(s):

Youssef Skandarani ◽

Pierre-Marc Jodoin ◽

Alain Lalande

Keyword(s):

Deep Learning ◽

Cardiac Mri ◽

Expert Knowledge ◽

Medical Image Analysis ◽

Ground Truth ◽

Cine Mri ◽

Data Sets ◽

Mri Segmentation ◽

Segmentation Evaluation ◽

Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

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Abstract 20: Deep Learning-Based Automated Intracranial Hemorrhage Detection and Notification

Stroke ◽

10.1161/str.51.suppl_1.20 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Benjamin Zahneisen ◽

Matus Straka ◽

Shalini Bammer ◽

Greg Albers ◽

Roland Bammer

Keyword(s):

Deep Learning ◽

Sensitivity And Specificity ◽

Intracranial Hemorrhage ◽

Health Care Professionals ◽

Gold Standard ◽

Ground Truth ◽

Mobile App ◽

Training Dataset ◽

Convolutional Network ◽

Ground Truth Data

Introduction: Ruling out hemorrhage (stroke or traumatic) prior to administration of thrombolytics is critical for Code Strokes. A triage software that identifies hemorrhages on head CTs and alerts radiologists would help to streamline patient care and increase diagnostic confidence and patient safety. ML approach: We trained a deep convolutional network with a hybrid 3D/2D architecture on unenhanced head CTs of 805 patients. Our training dataset comprised 348 positive hemorrhage cases (IPH=245, SAH=67, Sub/Epi-dural=70, IVH=83) (128 female) and 457 normal controls (217 female). Lesion outlines were drawn by experts and stored as binary masks that were used as ground truth data during the training phase (random 80/20 train/test split). Diagnostic sensitivity and specificity were defined on a per patient study level, i.e. a single, binary decision for presence/absence of a hemorrhage on a patient’s CT scan. Final validation was performed in 380 patients (167 positive). Tool: The hemorrhage detection module was prototyped in Python/Keras. It runs on a local LINUX server (4 CPUs, no GPUs) and is embedded in a larger image processing platform dedicated to stroke. Results: Processing time for a standard whole brain CT study (3-5mm slices) was around 2min. Upon completion, an instant notification (by email and/or mobile app) was sent to users to alert them about the suspected presence of a hemorrhage. Relative to neuroradiologist gold standard reads the algorithm’s sensitivity and specificity is 90.4% and 92.5% (95% CI: 85%-94% for both). Detection of acute intracranial hemorrhage can be automatized by deploying deep learning. It yielded very high sensitivity/specificity when compared to gold standard reads by a neuroradiologist. Volumes as small as 0.5mL could be detected reliably in the test dataset. The software can be deployed in busy practices to prioritize worklists and alert health care professionals to speed up therapeutic decision processes and interventions.

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High-Throughput Ground Cover Classification of Perennial Ryegrass (Lolium Perenne L.) for the Estimation of Persistence in Pasture Breeding

Agronomy ◽

10.3390/agronomy10081206 ◽

2020 ◽

Vol 10 (8) ◽

pp. 1206

Author(s):

Chinthaka Jayasinghe ◽

Pieter Badenhorst ◽

Joe Jacobs ◽

German Spangenberg ◽

Kevin Smith

Keyword(s):

Lolium Perenne ◽

High Throughput ◽

Perennial Ryegrass ◽

Ground Cover ◽

Ground Truth ◽

Grass Species ◽

Abiotic And Biotic Stresses ◽

Ground Truth Data ◽

Lolium Perenne L ◽

Biotic Stresses

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of Australia and New Zealand. However, it can have poor persistence due to a low tolerance to both abiotic and biotic stresses. A major challenge in measuring persistence in pasture breeding is that the assessment of pasture survival depends on ranking populations based on manual ground cover estimation. Ground cover measurements may include senescent and living tissues and can be measured as percentages or fractional units. The amount of senescent pasture present in a sward may indicate changes in plant growth, development, and resistance to abiotic and biotic stresses. The existing tools to estimate perennial ryegrass ground cover are not sensitive enough to discriminate senescent ryegrass from soil. This study aimed to develop a more precise sensor-based phenomic method to discriminate senescent pasture from soil. Ground-based RGB images, airborne multispectral images, ground-based hyperspectral data, and ground truth samples were taken from 54 perennial ryegrass plots three years after sowing. Software packages and machine learning scripts were used to develop a pipeline for high-throughput data extraction from sensor-based platforms. Estimates from the high-throughput pipeline were positively correlated with the ground truth data (p < 0.05). Based on the findings of this study, we conclude that the RGB-based high-throughput approach offers a precision tool to assess perennial ryegrass persistence in pasture breeding programs. Improvements in the spatial resolution of hyperspectral and multispectral techniques would then be used for persistence estimation in mixed swards and other monocultures.

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DeepFRET: Rapid and automated single molecule FRET data classification using deep learning

10.1101/2020.06.26.173260 ◽

2020 ◽

Cited By ~ 1

Author(s):

Johannes Thomsen ◽

Magnus B. Sletfjerding ◽

Stefano Stella ◽

Bijoya Paul ◽

Simon Bo Jensen ◽

...

Keyword(s):

Deep Learning ◽

Structural Biology ◽

Single Molecule ◽

Resonance Energy Transfer ◽

Resonance Energy ◽

Ground Truth ◽

Real Data ◽

Ground Truth Data ◽

Single Molecule Fret ◽

Human Operators

AbstractSingle molecule Förster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRET’s capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.

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EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

Bioinformatics ◽

10.1093/bioinformatics/btaa225 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3863-3870

Author(s):

Mischa Schwendy ◽

Ronald E Unger ◽

Sapun H Parekh

Keyword(s):

Deep Learning ◽

Cell Biology ◽

Ground Truth ◽

Training Data ◽

Training Dataset ◽

Visual Cell ◽

Application Development ◽

Ground Truth Data ◽

Quantitative Image ◽

Nucleus Segmentation

Abstract Motivation Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. Results We present a new dataset, EVICAN—Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. Availability and implementation The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.

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Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images

Applied Sciences ◽

10.3390/app10228285 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8285

Author(s):

Francesco Martino ◽

Domenico D. Bloisi ◽

Andrea Pennisi ◽

Mulham Fawakherji ◽

Gennaro Ilardi ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Deep Learning ◽

Oral Cancer ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Ground Truth ◽

Lesion Segmentation ◽

Ground Truth Data ◽

Tcga Dataset

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.

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Customized Synthetic Dataset for Deep Learning Noise Filtering for Time-of-Flight Indoor Navigation Applications

Advances in Transdisciplinary Engineering - Transdisciplinary Engineering for Complex Socio-technical Systems – Real-life Applications ◽

10.3233/atde200110 ◽

2020 ◽

Author(s):

Vinícius da Silva Ramalho ◽

Rômulo Francisco Lepinsk Lopes ◽

Ricardo Luhm Silva ◽

Marcelo Rudek

Keyword(s):

Deep Learning ◽

Random Noise ◽

Time Of Flight ◽

Ground Truth ◽

Noise Removal ◽

Synthetic Dataset ◽

Ground Truth Data ◽

Performance Benchmarking ◽

Depth Images ◽

Synthetic Datasets

Synthetic datasets have been used to train 2D and 3D image-based deep learning models, and they serve as also as performance benchmarking. Although some authors already use 3D models for the development of navigation systems, their applications do not consider noise sources, which affects 3D sensors. Time-of-Flight sensors are susceptible to noise and conventional filters have limitations depending on the scenario it will be applied. On the other hand, deep learning filters can be more invariant to changes and take into consideration contextual information to attenuate noise. However, to train a deep learning filter a noiseless ground truth is required, but highly accurate hardware would be need. Synthetic datasets are provided with ground truth data, and similar noise can be applied to it, creating a noisy dataset for a deep learning approach. This research explores the training of a noise removal application using deep learning trained only with the Flying Things synthetic dataset with ground truth data and applying random noise to it. The trained model is validated with the Middlebury dataset which contains real-world data. The research results show that training the deep learning architecture for noise removal with only a synthetic dataset is capable to achieve near state of art performance, and the proposed model is able to process 12bit resolution depth images instead of 8bit images. Future studies will evaluate the algorithm performance regarding real-time noise removal to allow embedded applications.

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Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

The Open Biomedical Engineering Journal ◽

10.2174/1874120701913010120 ◽

2019 ◽

Vol 13 (1) ◽

pp. 120-126

Author(s):

K. Bhavanishankar ◽

M. V. Sudhamani

Keyword(s):

Deep Learning ◽

Lung Nodule ◽

Ground Truth ◽

Lung Nodules ◽

Learning Approach ◽

Data Sets ◽

Ground Truth Data ◽

Series Of Experiments ◽

Sensitivity Specificity

Objective: Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification. Investigation/Methodology: Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets. Results: The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy. Conclusion: In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

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A NEW STEREO DENSE MATCHING BENCHMARK DATASET FOR DEEP LEARNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-405-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 405-412

Author(s):

T. Wu ◽

B. Vallet ◽

M. Pierrot-Deseilligny ◽

E. Rupnik

Keyword(s):

Deep Learning ◽

Ground Truth ◽

Fine Tuning ◽

Training Dataset ◽

Ground Truth Data ◽

Aerial Photogrammetry ◽

Dense Matching ◽

Benchmark Datasets ◽

3D Scene ◽

The Impact

Abstract. Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper, we publish a stereo dense matching dataset based on ISPRS Vaihingen dataset, and use it to evaluate some traditional and deep learning based methods. The evaluation shows that learning-based methods outperform traditional methods significantly when the fine tuning is done on a similar landscape. The benchmark also investigates the impact of the base to height ratio on the performance of the evaluated methods. The dataset can be found in https://github.com/whuwuteng/benchmark_ISPRS2021.

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HD Spot: Interpretable Deep Learning Classification of Single Cell Transcript Data

10.1101/822759 ◽

2019 ◽

Cited By ~ 1

Author(s):

Eric Prince ◽

Todd C. Hankinson

Keyword(s):

Deep Learning ◽

Single Cell ◽

High Throughput ◽

Ground Truth ◽

Sequencing Technologies ◽

Bioinformatic Tool ◽

Complex Relationships ◽

Insight Into ◽

Generation Sequencing

ABSTRACTHigh throughput data is commonplace in biomedical research as seen with technologies such as single-cell RNA sequencing (scRNA-seq) and other Next Generation Sequencing technologies. As these techniques continue to be increasingly utilized it is critical to have analysis tools that can identify meaningful complex relationships between variables (i.e., in the case of scRNA-seq: genes) in a way such that human bias is absent. Moreover, it is equally paramount that both linear and non-linear (i.e., one-to-many) variable relationships be considered when contrasting datasets. HD Spot is a deep learning-based framework that generates an optimal interpretable classifier a given high-throughput dataset using a simple genetic algorithm as well as an autoencoder to classifier transfer learning approach. Using four unique publicly available scRNA-seq datasets with published ground truth, we demonstrate the robustness of HD Spot and the ability to identify ontologically accurate gene lists for a given data subset. HD Spot serves as a bioinformatic tool to allow novice and advanced analysts to gain complex insight into their respective datasets enabling novel hypotheses development.

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