Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.

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Classifying exoplanet candidates with convolutional neural networks: application to the Next Generation Transit Survey

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz2058 ◽

2019 ◽

Vol 488 (4) ◽

pp. 5232-5250 ◽

Cited By ~ 2

Author(s):

Alexander Chaushev ◽

Liam Raynard ◽

Michael R Goad ◽

Philipp Eigmüller ◽

David J Armstrong ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Network Performance ◽

Area Under The Curve ◽

Simulated Data ◽

Real Data ◽

Training Data ◽

Next Generation ◽

Data Set ◽

Time Required

ABSTRACT Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that convolutional neural networks (CNN) provide an efficient solution to these problems. Here, we apply a CNN to classify planet candidates from the Next Generation Transit Survey (NGTS). For training data sets we compare both real data with injected planetary transits and fully simulated data, as well as how their different compositions affect network performance. We show that fewer hand labelled light curves can be utilized, while still achieving competitive results. With our best model, we achieve an area under the curve (AUC) score of $(95.6\pm {0.2}){{\ \rm per\ cent}}$ and an accuracy of $(88.5\pm {0.3}){{\ \rm per\ cent}}$ on our unseen test data, as well as $(76.5\pm {0.4}){{\ \rm per\ cent}}$ and $(74.6\pm {1.1}){{\ \rm per\ cent}}$ in comparison to our existing manual classifications. The neural network recovers 13 out of 14 confirmed planets observed by NGTS, with high probability. We use simulated data to show that the overall network performance is resilient to mislabelling of the training data set, a problem that might arise due to unidentified, low signal-to-noise transits. Using a CNN, the time required for vetting can be reduced by half, while still recovering the vast majority of manually flagged candidates. In addition, we identify many new candidates with high probabilities which were not flagged by human vetters.

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Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists

BMJ Open ◽

10.1136/bmjopen-2019-036423 ◽

2020 ◽

Vol 10 (9) ◽

pp. e036423

Author(s):

Zhigang Song ◽

Chunkai Yu ◽

Shuangmei Zou ◽

Wenmiao Wang ◽

Yong Huang ◽

...

Keyword(s):

Deep Learning ◽

Colorectal Adenoma ◽

Detection System ◽

Model Performance ◽

Area Under The Curve ◽

Learning Model ◽

Feature Maps ◽

Adenoma Detection ◽

Microscopic Evaluation ◽

Deep Learning Model

ObjectivesThe microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathologists before we put them into practical scenarios. The simple criteria of colorectal adenoma diagnosis make it to be a perfect testbed for this study.DesignThe deep learning model was trained by 177 accurately labelled training slides (156 with adenoma). The detailed labelling was performed on a self-developed annotation system based on iPad. We built the model based on DeepLab v2 with ResNet-34. The model performance was tested on 194 test slides and compared with five pathologists. Furthermore, the generalisation ability of the learning model was tested by extra 168 slides (111 with adenoma) collected from two other hospitals.ResultsThe deep learning model achieved an area under the curve of 0.92 and obtained a slide-level accuracy of over 90% on slides from two other hospitals. The performance was on par with the performance of experienced pathologists, exceeding the average pathologist. By investigating the feature maps and cases misdiagnosed by the model, we found the concordance of thinking process in diagnosis between the deep learning model and pathologists.ConclusionsThe deep learning model for colorectal adenoma diagnosis is quite similar to pathologists. It is on-par with pathologists’ performance, makes similar mistakes and learns rational reasoning logics. Meanwhile, it obtains high accuracy on slides collected from different hospitals with significant staining configuration variations.

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A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Electronics ◽

10.3390/electronics8090944 ◽

2019 ◽

Vol 8 (9) ◽

pp. 944 ◽

Cited By ~ 4

Author(s):

Heesin Lee ◽

Joonwhoan Lee

Keyword(s):

Deep Learning ◽

Image Quality ◽

Imaging System ◽

Scatter Correction ◽

Similarity Index ◽

Real Data ◽

Correction Method ◽

Training Data ◽

X Ray ◽

Scatter Reduction

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

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Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: experiments on osteosarcoma subtypes

10.1101/2020.09.10.20192294 ◽

2020 ◽

Author(s):

Haiming Tang ◽

Nanfei Sun ◽

Steven Shen

Keyword(s):

Deep Learning ◽

Model Performance ◽

High Variability ◽

Training Data ◽

Classification Model ◽

Training Dataset ◽

Learning Models ◽

Diagnostic Pathology ◽

Model Generalization ◽

Histopathological Images

Artificial intelligence (AI) has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images. We use the example of Osteosarcoma to illustrate the pitfalls and how the addition of model input variability can help improve model performance. We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. The performance of the model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting.We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation. We show the additions of more and more subtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances. In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.

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Deep learning based restoration of lost sections in Micro-CT core plugs

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1201/1/012070 ◽

2021 ◽

Vol 1201 (1) ◽

pp. 012070

Author(s):

S S Arsenyev-Obraztsov ◽

G O Plusch

Keyword(s):

Deep Learning ◽

Expert Knowledge ◽

Micro Ct ◽

Micro Computed Tomography ◽

Problem Statement ◽

Learning Methods ◽

Natural Choice ◽

Variational Autoencoder ◽

Digital Core ◽

Enormous Amount

Abstract Lack of petrophysical information is critical for reservoirs development composed of poorly consolidated rocks or for zones bearing wells with core damaged by improper coring operations. The restoration complexity of the digital-core lost sections is associated with the need to consider an enormous amount of data from the existing core image and the necessity to include lithological expert knowledge. That makes deep learning methods a natural choice for solving such problems. We proposed, examined, and compared several deep learning methods convenient for analyzing micro-computed tomography digital core data. It was done under the most simplistic problem statement when the destroyed part (a set of slices) is completely lost. Here, we present the results of comparison interpolation/extrapolation procedures under proposed quality metrics. We discover that the variational autoencoder method can be trained to extract some petrophysical parameters from the digital core plug in an unsupervised manner.

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A Deep Learning Approach for Rapid Mutational Screening in Melanoma

10.1101/610311 ◽

2019 ◽

Cited By ~ 10

Author(s):

Randie H. Kim ◽

Sofia Nomikou ◽

Nicolas Coudray ◽

George Jour ◽

Zarmeena Dawood ◽

...

Keyword(s):

Deep Learning ◽

Structural Characteristics ◽

Tumor Biology ◽

Model Performance ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Clinical Settings ◽

Mutational Screening ◽

Cancer Genome Atlas ◽

Whole Slide Images

AbstractImage-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

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Neural Supermodeling

10.5194/egusphere-egu2020-12601 ◽

2020 ◽

Author(s):

Wim Wiegerinck

Keyword(s):

Deep Learning ◽

Domain Knowledge ◽

Model Performance ◽

Ground Truth ◽

Training Data ◽

Combination Method ◽

Neural Network Approach ◽

Modeling Approach ◽

Promising Tool ◽

Spatially Extended

Deep learning is a modeling approach that has shown impressive results in image processing and is arguably a promising tool for dealing with spatially extended complex systems such earth atmosphere with its visually interpretable patterns. A disadvantage of the neural network approach is that it typically requires an enormous amount of training data.&#160;Another recently proposed modeling approach is supermodeling. In supermodeling it is assumed that a dynamical system &#8211; the truth &#8211; is modelled by a set of good but imperfect models. The idea is to improve model performance by dynamically combining imperfect models during the simulation. The resulting combination of models is called the supermodel. The combination strength has to be learned from data. However, since supermodels do not start from scratch, but make use of existing domain knowledge, they may learn from less data.&#160;One of the ways to combine models is to define the tendencies of the supermodel as linear (weighted) combinations of the imperfect model tendencies. Several methods including linear regression have been proposed to optimize the weights. &#160;However, the combination method might also be nonlinear. In this work we propose and explore a novel combination of deep learning and supermodeling, in which convolutional neural networks are used as tool to combine the predictions of the imperfect models. &#160;The different supermodeling strategies are applied in simulations in a controlled environment with a three-level, quasi-geostrophic spectral model that serves as ground truth and perturbed models that serve as the imperfect models.

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Deep learning of contagion dynamics on complex networks

Nature Communications ◽

10.1038/s41467-021-24732-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Charles Murphy ◽

Edward Laurence ◽

Antoine Allard

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Time Series Data ◽

Real Data ◽

Training Data ◽

Series Data ◽

Complementary Approach ◽

Quantitative Accuracy ◽

Simplifying Assumptions ◽

Effective Models

AbstractForecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

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Automated segmentation of insect anatomy from micro-CT images using deep learning

10.1101/2021.05.29.446283 ◽

2021 ◽

Author(s):

Evropi Toulkeridou ◽

Carlos Enrique Gutierrez ◽

Daniel Baum ◽

Kenji Doya ◽

Evan P Economo

Keyword(s):

Deep Learning ◽

Brain Area ◽

Three Dimensional ◽

Neural System ◽

Ct Images ◽

Automated Segmentation ◽

Micro Ct ◽

Micro Computed Tomography ◽

Anatomical Structures ◽

Trained Network

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a testcase. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union(IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes.Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

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Using Slit-Lamp Images for Deep Learning–Based Identification of Bacterial and Fungal Keratitis

10.20944/preprints202105.0438.v1 ◽

2021 ◽

Author(s):

Ning Hung ◽

Eugene Yu-Chuan Kang ◽

Andy Guan-Yu Shih ◽

Chi-Hung Lin ◽

Ming‐Tse Kuo ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Accuracy ◽

Learning Algorithm ◽

Model Performance ◽

Area Under The Curve ◽

Fungal Keratitis ◽

Classification Model ◽

Mapping Technique ◽

Slit Lamp ◽

Deep Learning Algorithm

In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.

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