Imbalance Learning and Its Application on Medical Datasets

To gain more valuable information from the increasing large amount of data, data mining has been a hot topic that attracts growing attention in this two decades. One of the challenges in data mining is imbalance learning, which refers to leaning from imbalanced datasets. The imbalanced datasets is dominated by some classes (majority) and other under-represented classes (minority). The imbalanced datasets degrade the learning ability of traditional methods, which are designed on the assumption that all classes are balanced and have equal misclassification costs, leading to the poor performance on the minority classes. This phenomenon is usually called the class imbalance problem. However, it is usually the minority classes of more interest and importance, such as sick cases in the medical dataset. Additionally, traditional methods are optimized to achieve maximum accuracy, which is not suitable for evaluating the performance on imbalanced datasets. From the view of data space, class imbalance could be classified as extrinsic imbalance and intrinsic imbalance. Extrinsic imbalance is caused by external factors, such as data transmission or data storage, while intrinsic imbalance means the dataset is inherently imbalanced due to its nature.  As extrinsic imbalance could be fixed by collecting more samples, this thesis mainly focus on on two scenarios of the intrinsic imbalance,  machine learning for imbalanced structured datasets and deep learning for imbalanced image datasets.  Normally, the solutions for the class imbalance problem are named as imbalance learning methods, which could be grouped into data-level methods (re-sampling), algorithm-level (re-weighting) methods and hybrid methods. Data-level methods modify the class distribution of the training dataset to create balanced training sets, and typical examples are over-sampling and under-sampling. Instead of modifying the data distribution, algorithm-level methods adjust the misclassification cost to alleviate the class imbalance problem, and one typical example is cost sensitive methods. Hybrid methods usually combine data-level methods and algorithm-level methods. However, existing imbalance learning methods encounter different kinds of problems. Over-sampling methods increase the minority samples to create balanced training sets, which might lead the trained model overfit to the minority class. Under-sampling methods create balanced training sets by discarding majority samples, which lead to the information loss and poor performance of the trained model. Cost-sensitive methods usually need assistance from domain expert to define the misclassification costs which are task specified. Thus, the generalization ability of cost-sensitive methods is poor. Especially, when it comes to the deep learning methods under class imbalance, re-sampling methods may introduce large computation cost and existing re-weighting methods could lead to poor performance. The object of this dissertation is to understand features difference under class imbalance, to improve the classification performance on structured datasets or image datasets. This thesis proposes two machine learning methods for imbalanced structured datasets and one deep learning method for imbalance image datasets. The proposed methods are evaluated on several medical datasets, which are intrinsically imbalanced.  Firstly, we study the feature difference between the majority class and the minority class of an imbalanced medical dataset, which is collected from a Chinese hospital. After data cleaning and structuring, we get 3292 kidney stone cases treated by Percutaneous Nephrolithonomy from 2012 to 2019. There are 651 (19.78% ) cases who have postoperative complications, which makes the complication prediction an imbalanced classification task. We propose a sampling-based method SMOTE-XGBoost and implement it to build a postoperative complication prediction model. Experimental results show that the proposed method outperforms classic machine learning methods. Furthermore, traditional prediction models of Percutaneous Nephrolithonomy are designed to predict the kidney stone status and overlook complication related features, which could degrade their prediction performance on complication prediction tasks. To this end, we merge more features into the proposed sampling-based method and further improve the classification performance. Overall, SMOTE-XGBoost achieves an AUC of 0.7077 which is 41.54% higher than that of S.T.O.N.E. nephrolithometry, a traditional prediction model of Percutaneous Nephrolithonomy. After reviewing the existing machine learning methods under class imbalance, we propose a novel ensemble learning approach called Multiple bAlance Subset Stacking (MASS). MASS first cuts the majority class into multiple subsets by the size of the minority set, and combines each majority subset with the minority set as one balanced subsets. In this way, MASS could overcome the problem of information loss because it does not discard any majority sample. Each balanced subset is used to train one base classifier. Then, the original dataset is feed to all the trained base classifiers, whose output are used to generate the stacking dataset. One stack model is trained by the staking dataset to get the optimal weights for the base classifiers. As the stacking dataset keeps the same labels as the original dataset, which could avoid the overfitting problem. Finally, we can get an ensembled strong model based on the trained base classifiers and the staking model. Extensive experimental results on three medical datasets show that MASS outperforms baseline methods.  The robustness of MASS is proved over implementing different base classifiers. We design a parallel version MASS to reduce the training time cost. The speedup analysis proves that Parallel MASS could reduce training time cost greatly when applied on large datasets. Specially, Parallel MASS reduces 101.8% training time compared with MASS at most in our experiments.  When it comes to the class imbalance problem of image datasets, existing imbalance learning methods suffer from the problem of large training cost and poor performance.  After introducing the problem of implementing resampling methods on image classification tasks, we demonstrate issues of re-weighting strategy using class frequencies through the experimental result on one medical image dataset.  We propose a novel re-weighting method Hardness Aware Dynamic loss to solve the class imbalance problem of image datasets. After each training epoch of deep neural networks, we compute the classification hardness of each class. We will assign higher class weights to the classes have large classification hardness values and vice versa in the next epoch. In this way, HAD could tune the weight of each sample in the loss function dynamically during the training process. The experimental results prove that HAD significantly outperforms the state-of-the-art methods. Moreover, HAD greatly improves the classification accuracies of minority classes while only making a small compromise of majority class accuracies. Especially, HAD loss improves 10.04% average precision compared with the best baseline, Focal loss, on the HAM10000 dataset. At last, I conclude this dissertation with our contributions to the imbalance learning, and provide an overview of potential directions for future research, which include extensions of the three proposed methods, development of task-specified algorithms, and fixing the challenges of within-class imbalance.2021-06-0

Three-Dimensional Assembly of PtNi Alloy Nanosticks with Enhanced Electrocatalytic Activity and Ultrahigh Stability for the Oxygen Reduction Reaction

A three-dimensional (3D) assembly of PtNi alloy nanosticks (NSA) is synthesized through an effective organic solvothermal approach to enhance the specific activity and long-term durability for the oxygen reduction reaction (ORR). The 3D PtNi NSA is composed of interconnected nanosticks with an average diameter of approximately 5 nm, which are confirmed to be of high crystallinity with ordered atomic arrangement of ORR-favorable PtNi (111). After an electrochemical dealloying process, the surface of the nanosticks becomes rough with a large quantity of step-like nanostructures, which are verified to effectively improve the ORR activity. The electrochemical results of half-cell tests demonstrate that the 3D PtNi alloy NSA exhibits a 4.5 times higher mass activity than the Pt/C (20 wt%, JM) catalyst (0.58 A/mg) and a 5.1 times higher specific activity (742 mu A/cm(2)). Most importantly, after the accelerated deterioration test, the 3D PtNi alloy NSA catalyst shows almost no activity decrease, both in half- and single-cell tests. The 3D PtNi alloy NSA prepared here is indeed a promising electrocatalyst for practical electrocatalytic applications

Improvement of PEMFC water management by employing water transport plate as bipolar plate

In this study, a porous hydrophilic water transport plate (WTP) has been employed as a bipolar plate to improve water management in proton exchange membrane fuel cells (PEMFCs). The electric conductivity, gas blocking property, water permeability and wettability of the WTP were characterized. The performance, electrochemical impedance spectroscopy (EIS) and water balance of fuel cells with WTPs and solid plates were evaluated. Benefiting from the humidification and drainage functions of the WTP, the performance of fuel cells with WTPs significantly improved compared with fuel cells with traditional solid plates. As indicated from the experiments, a WTP that was placed on the cathode side is favorable for cell performance and system complexity. Additionally, hydrogen stoichiometry hardly affects the water transport, whereas a decrease in air stoichiometry can switch the main function of the WTP from humidification to water drainage. The results show that the use of WTP technology is promising for water management improvement in PEMFCs. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved

High-Performance Low-Platinum Electrode for Proton Exchange Membrane Fuel Cells: Pulse Electrodeposition of Pt on Pd/C Nanofiber Mat

A novel electrode (E-P electrode) with a nanofiber structure and Pd/C@dendritic Pt catalysts is prepared by using electrospinning and pulse electrodeposition (PED) techniques. The maximum power density of the E-P electrode is 1.43-fold larger than that of the conventional electrode at the same cathode Pt loadings of 0.1 mg cm(-1). Owing to the in situ deposition of dendritic Pt on the surface of Pd in the Pd/C nanofiber mat, almost all Pt catalysts are accessible for oxygen. The electronic tuning between Pd and Pt enhances the oxygen reduction reaction activity of Pt catalysts. The large Pt surface area of the E-P electrode mitigates the oxygen-transport resistance in comparison with that of the conventional electrode. After the accelerated degradation test for 10000 cyclic voltammetry cycles, the maximum power density of the E-P electrode only decreases by 12%. The long-term stability of the E-P electrode is ascribed to the Pd/C@dendritic Pt catalysts and nanofiber structure

Investigation of water transport in fuel cells using water transport plates and solid plates

Water management of proton exchange membrane fuel cells (PEMFCs) is of vital importance to achieve better performance and durability. In this study, porous hydrophilic water transport plates (WTPs) with different pore structures were prepared and employed to improve water management in PEMFCs. Polarization curves, electrochemical impedance spectroscopy (EIS) and water balance were tested to investigate the effect of pore structure on cell performance and water transport process. The results show that pore structure has little effect on drainage function due to excess liquid water flux of WTPs, while the membrane hydration is improved with increased surface evaporation rate of WTPs, resulting in better cell performance. The favorable cell performance shows that WTP is a promising technique to improve water management in PEMFCs

Quantitative Three-Dimensional Reconstructions of Excitatory Synaptic Boutons in Layer 5 of the Adult Human Temporal Lobe Neocortex: A Fine-Scale Electron Microscopic Analysis

Studies of synapses are available for different brain regions of several animal species including non-human primates, but comparatively little is known about their quantitative morphology in humans. Here, synaptic boutons in Layer 5 (L5) of the human temporal lobe (TL) neocortex were investigated in biopsy tissue, using fine-scale electron microscopy, and quantitative three-dimensional reconstructions. The size and organization of the presynaptic active zones (PreAZs), postsynaptic densities (PSDs), and that of the 3 distinct pools of synaptic vesicles (SVs) were particularly analyzed. L5 synaptic boutons were medium-sized (~6 μm2) with a single but relatively large PreAZ (~0.3 μm2). They contained a total of ~1500 SVs/bouton, ~20 constituting the putative readily releasable pool (RRP), ~180 the recycling pool (RP), and the remainder, the resting pool. The PreAZs, PSDs, and vesicle pools are ~3-fold larger than those of CNS synapses in other species. Astrocytic processes reached the synaptic cleft and may regulate the glutamate concentration. Profound differences exist between synapses in human TL neocortex and those described in various species, particularly in the size and geometry of PreAZs and PSDs, the large RRP/RP, and the astrocytic ensheathment suggesting high synaptic efficacy, strength, and modulation of synaptic transmission at human synapses

enhancedelectrocatalyticperformanceofultrathinptnialloynanowiresforoxygenreductionreaction

In this paper, ultrathin Pt nanowires （Pt NWs） and PtNi alloy nanowires （PtNi NWs） supported on carbon were synthesized as electrocatalysts for oxygen reduction reaction （ORR）. Pt and PtNi NWs catalysts composed of interconnected nanoparticles were prepared by using a soft template method with CTAB as the surface active agent. The physical characterization and electrocatalytic perfor- mance of Pt NWs and PtNi NWs catalysts for ORR were investigated and the results were compared with the commercial Pt/C catalyst. The atomic ratio of Pt and Ni in PtNi alloy was approximately 3 to 1. The results show that after alloying with Ni, the binding energy of Pt shifts to higher values, indicating the change of its electronic structure, and that Pt3Ni NWs catalyst has a significantly higher electrocatalytic activity and good stability for ORR as compared to Pt NWs and even Pt/C catalyst. The enhanced electrocatalytic activity of Pt3Ni NWs catalyst is mainly resulted from the downshifted-band center of Pt caused by the interaction between Pt and Ni in the alloy, which facilitates the desorption of oxygen containing species （Oads or OHads） and the release of active sites

One-pot facile synthesis of PtCu coated nanoporous gold with unique catalytic activity toward the oxygen reduction reaction

A facile one-pot protocol to fabricate a PtCu coated nanoporous gold (NPG) catalyst (PtCu@NPG) is described here. PtCu@NPG prepared by this novel method not only preserves the NPG 3-D nanostructure but it also presents a unique catalytic activity and durability toward the oxygen reduction reaction

Highly stable nanostructured membrane electrode assembly based on Pt/Nb2O5 nanobelts with reduced platinum loading for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are promising candidates for the next-generation power sources; however, poor durability and high cost impede their widespread application. To address this dilemma, a nanostructured membrane electrode assembly (MEA) based on Pt/Nb2O5 nanobelts (NBs) was constructed through hydrothermal synthesis and the physical vapour deposition method. Pt/Nb2O5 NBs were directly aligned with Nafion membrane without ionomer as a binder. The prepared catalyst layer is ultrathin and has ultralow Pt loading. A single cell performance of 5.80 kW g(Pt)(-1) (cathode) and 12.03 kW g(Pt)(-1) (anode) was achieved by the Pt/Nb2O5 NBs-based MEA (66.0 mu g(Pt) cm(-2)). The accelerated durability test indicates that the Pt/Nb2O5 NBs-based MEA is far more stable than conventional Pt/C-based MEA