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

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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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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Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2705 ◽

2021 ◽

Vol 263 (2) ◽

pp. 4441-4445

Author(s):

Hyunsuk Huh ◽

Seungchul Lee

Keyword(s):

Deep Learning ◽

Background Noise ◽

Ground Truth ◽

Noise Removal ◽

Industrial Manufacturing ◽

Proposed Model ◽

Audio Data ◽

Passive Noise ◽

Removal Technique ◽

Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

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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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Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

10.36227/techrxiv.12362249.v2 ◽

2020 ◽

Author(s):

Rui Fan ◽

Hengli Wang ◽

Bohuan Xue ◽

Huaiyang Huang ◽

Yuan Wang ◽

...

Keyword(s):

Large Scale ◽

Median Filter ◽

Ground Truth ◽

Depth Image ◽

Range Data ◽

3 Dimensional ◽

Depth Images ◽

Mesh Model ◽

Surface Normal ◽

Synthetic Datasets

Over the past decade, significant efforts have been made to improve the trade-off between speed and accuracy of surface normal estimators (SNEs). This paper introduces an accurate and ultrafast SNE for structured range data. The proposed approach computes surface normals by simply performing three filtering operations, namely, two image gradient filters (in horizontal and vertical directions, respectively) and a mean/median filter, on an inverse depth image or a disparity image. Despite the simplicity of the method, no similar method already exists in the literature. In our experiments, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3-dimensional (3D) mesh models. Each mesh model is used to generate 1800--2500 pairs of 480x640 pixel depth images and the corresponding surface normal ground truth from different views. The average angular errors with respect to the easy, medium and hard datasets are 1.6 degrees, 5.6 degrees and 15.3 degrees, respectively. Our C++ and CUDA implementations achieve a processing speed of over 260 Hz and 21 kHz, respectively. Our proposed SNE achieves a better overall performance than all other existing computer vision-based SNEs. Our datasets and source code are publicly available at: sites.google.com/view/3f2n.

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Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

10.36227/techrxiv.12362249.v1 ◽

2020 ◽

Author(s):

Rui Fan ◽

Hengli Wang ◽

Bohuan Xue ◽

Huaiyang Huang ◽

Yuan Wang, ◽

...

Keyword(s):

Large Scale ◽

Median Filter ◽

Ground Truth ◽

Depth Image ◽

Range Data ◽

3 Dimensional ◽

Depth Images ◽

Mesh Model ◽

Surface Normal ◽

Synthetic Datasets

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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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Unsupervised Deep Learning-Based RGB-D Visual Odometry

Applied Sciences ◽

10.3390/app10165426 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5426 ◽

Cited By ~ 1

Author(s):

Qiang Liu ◽

Haidong Zhang ◽

Yiming Xu ◽

Li Wang

Keyword(s):

Deep Learning ◽

Feature Matching ◽

Ground Truth ◽

Visual Odometry ◽

Depth Images ◽

Network Training ◽

Stream Structure ◽

Unsupervised Deep Learning ◽

Rgb Images ◽

Learning Frameworks

Recently, deep learning frameworks have been deployed in visual odometry systems and achieved comparable results to traditional feature matching based systems. However, most deep learning-based frameworks inevitably need labeled data as ground truth for training. On the other hand, monocular odometry systems are incapable of restoring absolute scale. External or prior information has to be introduced for scale recovery. To solve these problems, we present a novel deep learning-based RGB-D visual odometry system. Our two main contributions are: (i) during network training and pose estimation, the depth images are fed into the network to form a dual-stream structure with the RGB images, and a dual-stream deep neural network is proposed. (ii) the system adopts an unsupervised end-to-end training method, thus the labor-intensive data labeling task is not required. We have tested our system on the KITTI dataset, and results show that the proposed RGB-D Visual Odometry (VO) system has obvious advantages over other state-of-the-art systems in terms of both translation and rotation errors.

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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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