deep learning regression
210332685
Biomarker Localization From Deep Learning Regression Networks
Biomarker estimation methods from medical images have traditionally followed a segment and measure strategy. Deep learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black box. In this work, we present a novel deep learning network structure that, when trained with only the value of the biomarker, can perform biomarker regression and the generation of an accurate localization mask simultaneously, thus enabling a qualitative assessment of the image locus that relates to the quantitative result. We showcase the proposed method with three different network structures and compare their performance against direct regression networks in four different problems: pectoralis muscle area (PMA) subcutaneous fat area (SFA) liver mass area in single slice computed tomography (CT) and Agatston score estimated from non contrast thoracic CT images (CAC) Our results show that the proposed method improves the performance with respect to direct biomarker regression methods (correlation coefficient of 0.978, 0.998, and 0.950 for the proposed method in comparison to 0.971, 0.982, and 0.936 for the reference regression methods on PMA, SFA and CAC respectively) while achieving good localization (DICE coefficients of 0.875, 0.914 for PMA and SFA respectively, p 0.05 for all pairs) We observe the same improvement in regression results comparing the proposed method with those obtained by quantify the outputs using an U Net segmentation network (0.989 and 0.951 respectively) We, therefore, conclude that it is possible to obtain simultaneously good biomarker regression and localization when training biomarker regression networks using only the biomarker value.
IEEE Transactions on Medical Imaging
Carlos Cano-Espinosa German Gonzalez George R Washko Miguel Cazorla Raul San Jose Estepar
221819500
Deep learning regression for inverse quantum scattering
In this work we study the inverse quantum scattering via deep learning regression, which is implemented via a Multilayer Perceptron. A step by step method is provided in order to obtain the potential parameters. A circular boundary wall potential was chosen to exemplify the method. Detailed discussion about the training is provided. A investigation with noisy data is presented and it is observed that the neural network is useful to predict the potential parameters.
A C Maioli
214772445
Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi omics integration.
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning based predictive model using Deep Denoising Auto encoder (DDAE) and Multi layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC) The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state of the art regression models. Finally, the deep learning based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.
Genomics
Dibyendu Bikash Seal Vivek Das Saptarsi Goswami Rajat Kumar De
218598509
Deep Learning Regression for Prostate Cancer Detection and Grading in Bi Parametric MRI
One of the most common types of cancer in men is prostate cancer (PCa) Biopsies guided by bi parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI. In this work, however, we present a neural network that simultaneously detects and grades cancer tissue in an end to end fashion. This is more clinically relevant than the classification goal of the ProstateX 2 challenge. We used the dataset of this challenge for training and testing. We use a 2D U Net with MRI slices as input and lesion segmentation maps that encode the Gleason Grade Group (GGG) a measure for cancer aggressiveness, as output. We propose a method for encoding the GGG in the model target that takes advantage of the fact that the classes are ordinal. Furthermore, we evaluate methods for incorporating prostate zone segmentations as prior information, and ensembling techniques. The model scored a voxel wise weighted kappa of <inline formula><tex math notation="LaTeX"$0.446 \pm 0.082$/tex math>/inline formula> and a Dice similarity coefficient for segmenting clinically significant cancer of <inline formula><tex math notation="LaTeX"$0.370 \pm 0.046$/tex math>/inline formula> obtained using 5 fold cross validation. The lesion wise weighted kappa on the ProstateX 2 challenge test set was <inline formula><tex math notation="LaTeX"$0.13 \pm 0.27$/tex math>/inline formula> We show that our proposed model target outperforms standard multiclass classification and multi label ordinal regression. Additionally, we present a comparison of methods for further improvement of the model performance.
IEEE Transactions on Biomedical Engineering
Coen de Vente Pieter C Vos Matin Hosseinzadeh Josien P W Pluim Mitko Veta
216493378
Robust Loss Function for Deep Learning Regression with Outliers
In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares (known as "L2" estimator and lead to unreliable results (due to the large abnormal error registered by outliers compared to the error of the majority of the training samples) To deal with this, several robust to outliers methods in robust statistics have been proposed in the statistical literature. However, in the context of deep regression networks, very few efforts have been carried out to deal with outliers and most deep regression algorithms make use of the traditional L2 loss function. In this paper, we consider the issue of training deep neural networks in the context of robust regression. As such, we introduce a robust deep regression model which is based on a novel robust loss function. With this latter, our model is able to adapt to an outlier distribution, without requiring any hard threshold on the proportion of outliers in the training set. Experimental evaluations on a head pose estimation dataset show that our model generalizes well to noisy datasets, compared to other state of the art techniques.
Lamyaa Sadouk Taoufiq Gadi El Hassan Essoufi
216432134
Interpretable deep learning regression for breast density estimation on MRI
Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorithms. These algorithms often rely on segmentation of breast and FGT volume. In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments. We assessed breast density in 506 patients with breast cancer using a regression convolutional neural network (CNN) The input for the CNN were slices of breast MRI of 128 x 128 voxels, and the output was a continuous density value between 0 (fatty breast) and 1 (dense breast) We used 350 patients to train the CNN, 75 for validation, and 81 for independent testing. We investigated why the CNN came to its predicted density using Deep SHapley Additive exPlanations (SHAP) The density predicted by the CNN on the testing set was significantly correlated with the ground truth densities (N 81 patients, Spearman's r 0:86, P 0:001) When inspecting what the CNN based its predictions on, we found that voxels in FGT commonly had positive SHAP values, voxels in fatty tissue commonly had negative SHAP values, and voxels in non breast tissue commonly had SHAP values near zero. This means that the prediction of density is based on the structures we expect it to be based on, namely FGT and fatty tissue. To conclude, we presented an interpretable deep learning regression method for breast density estimation on MRI with promising results.
Medical Imaging
Bas H M van der Velden Max A A Ragusi Markus H A Janse Claudette E Loo Kenneth G A Gilhuijs
220258649
Lithium ion Battery Life Cycle Prediction with Deep Learning Regression Model
This paper presents a data driven regression model to predict the life cycle of lithium ion battery. The model is built based on five different key features derived from discharging voltage, current, time, and skin temperature. These sets of data are commonly available in most Battery Management Systems (BMS) which makes the proposed regression model widely applicable to various battery systems. The five key feature based battery model is evaluated and validated all through the paper and is proved to have high prediction accuracy of battery life cycles. The proposed regression model can be utilized for real time life cycle prediction without the need of historical data.
2020 IEEE Applied Power Electronics Conference and Exposition (APEC)
Huawei Yang Yuan Cao Huaiqi Xie Shuai Shao Jie Zhao Tianyi Gao Jiajun Zhang Binghua Zhang
225046884
Volumetric breast density estimation on MRI using explainable deep learning regression
To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3 dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N 400) validation (N 50) and hold out test set (N 165) Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman's correlation and Bland Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP) Spearman's correlation between estimated and ground truth density was r 0.81 (N 165, P 0.001) in the hold out test set. The estimated density had a median bias of 0.70% (95% limits of agreement 6.8% to 5.0% to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.
Scientific reports
Bas H M van der Velden Markus H A Janse Max A A Ragusi Claudette E Loo Kenneth G A Gilhuijs
218515999
Audio visual deep learning regression of apparent personality
Personality perception is based on the relationship of the human being with the individuals of his surroundings. This kind of perception allows to obtain conclusions based on the analysis and interpretation of the observable, mainly face expressions, tone of voice and other nonverbal signals, allowing the construction of an apparent personality (or first impression) of people. Apparent personality (or first impressions) are subjective, and subjectivity is an inherent property of perception based exclusively on the point of view of each individual. In this project, we approximate such subjectivity using a multi modal deep neural network with audiovisual signals as input and a late fusion strategy of handcrafted features, achieving accurate results. The aim of this work is to perform an analysis of the influence of automatic prediction for apparent personality (based on the Big Five model) of the following characteristics: raw audio, visual information (sequence of face images) and high level features, including Ekman's universal basic emotions, gender and age. To this end, we have defined different modalities, performing combinations of them and determining how much they contribute to the regression of apparent personality traits. The most remarkable results obtained through the experiments performed are as follows: in all modalities, females have a higher average accuracy than men, except in the modality with only audio; for happy emotion, the best accuracy score is found in the Conscientiousness trait; Extraversion and Conscientiousness traits get the highest accuracy scores in almost all emotions; visual information is the one that most positively influences the results; the combination of high level features chosen slightly improves the accuracy performance for predictions. La percepcio de la personalitat es basa en la relacio de l'esser huma amb els individus del seu entorn. Aquest tipus de percepcio permet obtenir conclusions basades en l'analisi i interpretacio de l'observable, principalment expressions facials, to de veu i altres senyals no verbals, el que permet la construccio d'una personalitat aparent (o primera impressio) de les persones. Les primeres impressions son subjectives, i la subjectivitat es una propietat inherent de la percepcio basada exclusivament en el punt de vista de cada individu. En aquest projecte, aproximem aquesta subjectivitat utilitzant una xarxa neuronal profunda multimodal amb senyals audiovisuals com a entrada i una estrategia de fusio tardana de Handcrafted features, aconseguint resultats excel*lents. L'objectiu d'aquest treball es realitzar una analisi de la influencia de la prediccio automatica de la personalitat aparent (basada en el model Big Five) de les seguents caracteristiques: raw audio, informacio visual (sequencia d'imatges de cares) i high level features, incloses les emocions basiques universals d'Ekman, el genere i l'edat. Amb aquesta finalitat, hem definit diferents modalitats, realitzant combinacions d'elles i determinant quant contribueixen a la regressio dels trets de personalitat aparents. Els resultats mes notables obtinguts a traves dels experiments realitzats son els seguents: en totes les modalitats, les dones tenen una major precisio mitjana que els homes, excepte en la modalitat amb nomes audio; per l'emocio felic, la millor puntuacio de precisio es troba en el tret de Consciencia; els trets d'Extraversio i Consciencia obtenen les puntuacions de precisio mes alts en gairebe totes les emocions; la informacio visual es la que mes influeix positivament en els
Alejandro Alfonso Hernandez
228868486
Automatic velocity picking from semblances with a new deep learning regression strategy: Comparison with a classification approach
The physical basis, parameterization, and assumptions involved in root mean square (rms) velocity estimation have not significantly changed since they were first developed. However, these three aspects are all good targets for novel application of the recent emergence of machine learning (ML) Therefore, it is useful at this time to provide a tutorial overview of two state of the art ML implementations; we have designed and evaluated classification and regression neural networks for the extraction of apparent rms velocity trajectories from semblance data. Both networks share a similar end to end trainable structure, except for the final layer. In the classification network, the velocity picking is performed by finding the largest amplitude trajectory through all velocity bins. The regression network, on the other hand, applies a differentiable soft argmax function that converts the feature maps directly to apparent rms velocity values as functions of traveltime. Relative confidence maps can also be estimated from both neural networks. A large number of synthetic models with horizontal layers are created, and common midpoint gathers are simulated from those models as training samples. Transfer learning is applied to fine tune the networks with a small number of samples for testing with synthetic and field data from more complicated (2D) models. Tests using synthetic data show that the regression and classification networks can give reasonable velocity predictions from semblances, but the regression network gives higher accuracy.
Wenlong Wang George A McMechan Jianwei Ma Fei Xie