Deep learning for patient‐specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks

AbstractMotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/[email protected]

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Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.164168 ◽

2019 ◽

Vol 7 (6) ◽

pp. 164-168

Author(s):

Kshitij Tripathi ◽

Rajendra G. Vyas ◽

Anil K. Gupta

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Novel Approach

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CONVOLUTIONAL NEURAL NETWORKS, ANALYTICAL ALGORITHMS, AND PERSONALIZED HEALTH CARE: EMBRACING THE MASSIVE DATA ANALYSIS CAPABILITIES OF DEEP LEARNING ARTIFICIAL INTELLIGENCE SYSTEMS TO COMPLEMENT AND IMPROVE MEDICAL SERVICES

American Journal of Medical Research ◽

10.22381/ajmr5220187 ◽

2018 ◽

Vol 5 (2) ◽

pp. 52 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Health Care ◽

Deep Learning ◽

Data Analysis ◽

Convolutional Neural Networks ◽

Massive Data ◽

Personalized Health ◽

Personalized Health Care ◽

Artificial Intelligence Systems

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Applied Sciences ◽

10.3390/app11052284 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2284

Author(s):

Asma Maqsood ◽

Muhammad Shahid Farid ◽

Muhammad Hassan Khan ◽

Marcin Grzegorzek

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Red Blood Cells ◽

Convolutional Neural Networks ◽

Blood Cells ◽

Superior Performance ◽

Learning Models ◽

Infected Female ◽

The World ◽

Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

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Efficient downscaling of satellite oceanographic data with convolutional neural networks

SIGSPATIAL Special ◽

10.1145/3447994.3448011 ◽

2021 ◽

Vol 12 (3) ◽

pp. 46-47

Author(s):

Nikita Saxena

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Super Resolution ◽

Sea Surface ◽

Clear Sky ◽

Oceanographic Data ◽

Computationally Expensive ◽

Sky Conditions ◽

Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

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