Gleason Grade Group Prediction for Prostate Cancer Patients with MR Images Using Convolutional Neural Network

Purpose: Gleason Grading (GG) Grouping system is an important index in determining treatment plan or predicting outcome for prostate cancer patients. Unfortunately, currently GG Grouping results can only be obtained from biopsy-driven pathological tests. We aim to predict GG groups for PCa patients from multiparametric magnetic resonance images (mp-MRI). Methods: The challenges include data heterogeneity, small sample size and highly imbalanced distribution among different groups. A retrospective collection of 201 patients with 320 lesions from the SPIE-AAPM-NCI PROSTATEx Challenge (https://doi.org/10.7937/K9TCIA.2017.MURS5CL) was studied, among which only 98 patients with 110 lesions having GG available. And number of lesions from each group was 36, 39, 20, 8, and 7, respectively, for GG 1-5. We approached the challenging task by bridging though easier one of classifying 320 lesions into benign or malignant, and transferring learned knowledge to GG prediction on 110 lesions. During implementation, a four-convolutional neural network (CNN) was used for malignancy classification. To prevent over-fitting on small sample size, instead of fine-tuning on CNN, learned features were extracted and classified by weighted extreme learning machine (wELM), traditional classifier that assigned larger weight to samples from minority class.Image pre-processing included registration and normalization. Image rotation and scaling were also used to increase sample size and re-balance number of malignant and benign lesions. Results: The best combination of modalities as input to CNN was found to be T2W, apparent diffusion coefficient (ADC) and B-value maps (b=50 s/mm2). During phase 1 of CNN training, average and best results of (Sensitivity, Specificity, G-mean) over 10 folds were (0.53, 0.83, 0.65) and (1, 0.88, 0.91), respectively. Features from best performing model were extracted to represent each lesion, and those from the last convolutional layer were found constantly better than from all other layers (Table 1). This implies that semantic features regarding lesion information is more important than local and detailed features such as contrast change in GG prediction. Conclusion: This work has successfully tackled the challenging task of GG prediction from mp-MRI by bridging through an easier task and has combined feature extraction using deep learning model and small data classification using traditional classifier to benefit from both.https://scholarlycommons.henryford.com/merf2019basicsci/1003/thumbnail.jp

A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification

FEDSM2013-16598 NUMERICAL OPTIMAL DESIGN OF IMPELLER BACK PUMP-OUT VANES ON AXIAL THRUST IN CENTRIFUGAL PUMPS

ABSTRACT Axial thrust in centrifugal pu mps attracts extensive attention in order to improve the operating reliability of pu mps. High axial thrust can cause rapid thrust bearing wear and subsequent pump failure o r frequent overhauls. A centrifugal pump (XA65/20) was selected in this study, based on L 16 (4 3 ) orthogonal array and CFD methods. The time -averaged Navier-Stokes equation was calcu lated for a 3D steady flow in the model pump in ANSYS CFX with the standard k-ω turbulence model and standard wall function applied. The structured meshes with different numbers were used for comparison in order to confirm that the computational results were not influenced by the mesh. Meanwhile, the effects of impeller back pump -out vane geometrical parameters, including its thickness S k , its outlet diameter D e and axial clearance δ, on the axial thrust and performances of the model centrifugal pu mp were analy zed. The different orthogonal schemes were obtained on the different values of S k , D e , and δ. Finally, when the parameters of the impeller S k , D e , and δ are 5mm, 100mm, 1.5mm, respectively. The Best Efficiency Point (BEF) of 69.9% was achieved with 60.12m for the designed head and -952.133N for the minimu m total axial force. The corresponding impeller with minimu m total axial fo rce was considered as the optimal scheme and manufactured for experimental test. The external characteristics by CFD have a good agreement with their experimental data, wh ich also better verified the accuracy of the numerical method of axial thrust applied in this research. Back pump-out vane thickness Back pump-out vane outlet diameter Z Back pump-out vane number N (r/min) Rotating speed ρ (kg/m3) Density g (m/s2) Gravity acceleration H (m) Pressure head ω (rad/s) Angular speed p (Pa) Pressure INTRODUCTION One of the most challenging aspects in horizontal pumps design is represented by the accurate evaluation of the axial thrust acting on the rotating shaft. In order to balance axial thrust of centrifugal pu mps, many devices such as balancing disk, balancing dru m, balancing hole and sealing system are used In this paper, the model pu mp of XA65/20 was designed to study its axial thrust and external characteristics with S k , δ an

Optimization based extreme learning machine : applications and data-driven extensions

Artificial neural network, or commonly referred to as ''neural network'', is a successful example of how human nature has led technology. However, traditional learning algorithms in neural network require iterative parameter tuning and often suffer from problems like local minimum and slow convergence. Extreme learning machine (ELM) is able to overcome all the problems above. Proposed as a learning algorithm for the single-hidden layer feedforward neural networks (SLFNs), ELM was later extended to the ''generalized'' SLFNs where the hidden nodes might take wide types of forms not limited to neuron type. The main feature of ELM lies in the random hidden nodes. Moreover, the universal approximation theorem of ELM has guaranteed good performance as long as the hidden layer mapping is any bounded piecewise continuous function. Researchers on ELM have been seeking for some other methods to improve the generalization performance. Standard optimization method was thus considered in the realization of ELM. Not only better performance in classification was achieved, but also a fact was revealed that ELM and SVM are actually consistent from optimization point of view. The resultant ELM classifier based on standard optimization method was found with comparable performance as SVM. What's more, the implementation of ELM is much easier since the performance is insensitive to parameters. Afterwards, ELM was further analyzed from optimization point of view and solution of kernel version was derived. So far the unified framework of ELM has been formed that includes traditional neural networks, support vector networks, and regularized networks. Since the ELM theory is only developed since very recent years, there are plenty of places ELM can be applied. In this thesis, works of ELM successfully applied in real world applications, such as face recognition system and relevance ranking for information. In real world applications, the natural data is with different characteristics. For example, situations when data is not available at once or data is of large scale often arise. In this case, online sequential learning model of a machine learning technique is generally regarded as one typical solution. In this thesis, online sequential model based on ELM framework is provided so that not only all the advantages of ELM over other machine learning techniques are pertained but also the fore mentioned problems are solved. Another situation happens quite often is that the training data is not well balanced. Any normal machine learning technique that assumes well balanced data distribution is supposed with the tendency to bias the performance. In this case, weighted version of ELM is proposed as the most straightforward and efficient method to tackle such problem.DOCTOR OF PHILOSOPHY (EEE

Weighted extreme learning machine for imbalance learning

Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are “generalized” single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able to generalize to balanced data. The proposed method maintains the advantages from original ELM: (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users' needs, the weighted ELM can be generalized to cost sensitive learning

Prediction of Gleason Grade Group of Prostate Cancer on Multiparametric MRI using Deep Machine Learning Models

Background: The Gleason Grade (GG) Group system has been introduced recently for more accurate stratification of prostate cancer (PCa). The grading system is based on the histologic patterns which is accessed from needle core biopsy, therefore it could be negatively impacted by the intratumor heterogeneity. Objectives: We aim to develop a deep learning algorithm to predict GG groups using multiparametric magnetic resonance images (mp-MRI). Methods: We studied a retrospective collection of 201 patients with 320 lesions from the SPIE-AAPM-NCI PROSTATEx Challenge (https://doi.org/10.7937/K9TCIA.2017.MURS5CL), among which 98 patients with 110 lesions with GG available from biopsy. And the number of lesions in each subgroup was 36, 39, 20, 8, and 7, respectively, for GG 1-5. The images were acquired on two different types of Siemens 3T MR scanners. T2W images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800 s/mm2), and subsequently, the ADC map was calculated by the scanner software. Image pre-processing included registration and normalization. Image rotation and scaling were also used to increase the sample size and re-balance the number of lesions in various GG. To prevent over-fitting on a small sample size, we implemented a transfer learning model by carrying over the features learned from the malignancy classification of 320 lesions from our previous model into the GG prediction. And we replaced the end-to-end convolutional neural network (CNN) training model with a combination of feature extraction using CNN and classification using weighted extreme learning machine (wELM). Results: Features from the best performing model were extracted to represent each lesion, and those from the last convolutional layer were found constantly better than from all other layers. Based on 3-fold cross validation, the average validation results for sensitivity, specificity, positive predictive value, and negative predictive value for differentiation of each GG (1-5) were (1, 0.99, 0.97, 1), (0.69, 0.85, 0.73, 0.83), (0.9, 0.69, 0.46, 0.97), (0.89, 0.64, 0.16, 0.99), and (1, 0.78, 0.39, 1), respectively. GG4 had the highest false positive values. GG 3 was often misclassified as GG 4. Results of GG3-5 vs. GG1-2 were (0.82, 0.87, 0.76, 0.92). The stratification of GG4-5 vs. GG1-3 was (0.87, 0.81, 0.42, 0.98). Conclusions: This work has made substantial progress tackling the challenging task of GG prediction from mp-MRI due to a smaller and unbalanced data size by transferring knowledge from a malignancy classification task we developed earlier. The combined feature extraction using deep learning model and weighted extreme learning machine classifier has shown promising results for the GG prediction