scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

2019 ◽  
Vol 25 (6) ◽  
pp. 954-961 ◽  
Author(s):  
Diego Ardila ◽  
Atilla P. Kiraly ◽  
Sujeeth Bharadwaj ◽  
Bokyung Choi ◽  
Joshua J. Reicher ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Low Dose ◽  
Three Dimensional ◽  
Lung Cancer Screening ◽  
Chest Computed Tomography ◽  
End To End

scholarly journals Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

2019 ◽  
Vol 25 (8) ◽  
pp. 1319-1319 ◽  
Author(s):  
Diego Ardila ◽  
Atilla P. Kiraly ◽  
Sujeeth Bharadwaj ◽  
Bokyung Choi ◽  
Joshua J. Reicher ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Low Dose ◽  
Three Dimensional ◽  
Lung Cancer Screening ◽  
Chest Computed Tomography ◽  
End To End

scholarly journals Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

2020 ◽  
Vol 6 (12) ◽  
pp. 132
Author(s):  
Mathé T. Zeegers ◽  
Daniël M. Pelt ◽  
Tristan van Leeuwen ◽  
Robert van Liere ◽  
Kees Joost Batenburg
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Reduction ◽  
Hyperspectral Data ◽  
Reduction Step ◽  
Combining Data ◽  
Wide Range ◽  
Reduction Methods ◽  
End To End

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods.

scholarly journals End-to-end COVID-19 screening with 3D deep learning on chest computed tomography

2020 ◽  
Author(s):  
Kun Yang ◽  
Xinfeng Liu ◽  
Yingli Yang ◽  
Xiangjun Liao ◽  
Rongpin Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
False Negative ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Screening Process ◽  
End To End ◽  
Automated Screening ◽  
Validation Set ◽  
Novel Coronavirus

Abstract The outbreak of an acute respiratory syndrome (called novel coronavirus pneumonia, NCP) caused by SARS-CoV-2 virus has now progressed to a pandemic, and became the most common threat to public death worldwide[i],[ii]. COVID-19 screening using computed tomography (CT) can perform a quick diagnosis and identify high-risk NCP patients[iii]. Automated screening using CT volumes is a challenging task owing to inter-grader variability and high false-positive and false-negative rates. We propose a three dimensional (3D) deep learning convolutional neural networks (CNN) that use a patient’s CT volume to predict the risk of COVID-19, trained end-to-end from CT volumes directly, using only images and disease labels as inputs. Our model achieves a state-of-the-art performance (95.78% overall accuracy, 99.4% area under the curve) on a dataset of 1,684 COVID-19 patients, nearly twice larger than previous datasets3, and performs similarly on an independent clinical validation set of 121 cases. We tested its performance against six radiologists on clinical confirmed patient’ CT volumes, our model outperformed all six radiologists with absolute reductions of 7% in false positives and 35.9% in false negatives, demonstrating artificial intelligence (AI) capable to optimize the COVID-19 screening process via computer assistance and automation with a level of competence comparable to radiologists. While the vast majority of patients remain unscreened, we show the potential for AI to increase the accuracy and consistency of COVID-19 screening with CT.

scholarly journals A Review of Deep Learning Based Speech Synthesis

2019 ◽  
Vol 9 (19) ◽  
pp. 4050 ◽  
Author(s):  
Yishuang Ning ◽  
Sheng He ◽  
Zhiyong Wu ◽  
Chunxiao Xing ◽  
Liang-Jie Zhang
Keyword(s):  
Deep Learning ◽  
Speech Synthesis ◽  
Large Scale ◽  
Synthesis Methods ◽  
Research Directions ◽  
Feature Representations ◽  
Wide Range ◽  
New Frontier ◽  
Speech Interaction ◽  
End To End

Speech synthesis, also known as text-to-speech (TTS), has attracted increasingly more attention. Recent advances on speech synthesis are overwhelmingly contributed by deep learning or even end-to-end techniques which have been utilized to enhance a wide range of application scenarios such as intelligent speech interaction, chatbot or conversational artificial intelligence (AI). For speech synthesis, deep learning based techniques can leverage a large scale of <text, speech> pairs to learn effective feature representations to bridge the gap between text and speech, thus better characterizing the properties of events. To better understand the research dynamics in the speech synthesis field, this paper firstly introduces the traditional speech synthesis methods and highlights the importance of the acoustic modeling from the composition of the statistical parametric speech synthesis (SPSS) system. It then gives an overview of the advances on deep learning based speech synthesis, including the end-to-end approaches which have achieved start-of-the-art performance in recent years. Finally, it discusses the problems of the deep learning methods for speech synthesis, and also points out some appealing research directions that can bring the speech synthesis research into a new frontier.

scholarly journals Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique – initial clinical experience

2021 ◽  
Vol 10 (4) ◽  
pp. 205846012110083
Author(s):  
Tormund Njølstad ◽  
Anselm Schulz ◽  
Johannes C Godt ◽  
Helga M Brøgger ◽  
Cathrine K Johansen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
High Strength ◽  
Image Noise ◽  
Slice Thickness ◽  
Visual Grading ◽  
Abdominal Computed Tomography

Background A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. Purpose To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. Material and methods Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses. Results For 2.5 mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p < 0.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p < 0.001). This improved image quality was also observed for 0.625 mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5 mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p < 0.01). Conclusions Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria.

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