MPCE: A Maximum Probability Based Cross Entropy Loss Function for Neural Network Classification

Zhou, Yangfan; Wang, Xin; Zhang, Mingchuan; Zhu, Junlong; Zheng, Ruijuan; Wu, Qingtao

doi:10.1109/access.2019.2946264

Cited by 69 publications

(25 citation statements)

References 38 publications

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“…The following equations are used to express the softmax function: 38 where z i represents the phase i corresponding to the label value, and p i denotes the probability of this phase. To adapt the softmax classifier, the loss function use softmax cross-entropy loss function: 39 where y i and p i represent the label’s true value and the probability of the phase calculated by Eq. ( 1 ), respectively.…”

Section: Resultsmentioning

confidence: 99%

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

et al. 2021

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Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 28×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.

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Section: Resultsmentioning

confidence: 99%

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

et al. 2021

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“…The L CE is used to describe the distance of probability distributions between segmented venous thrombus and ground truth. Employing L CE can segment high intensity and large area thrombus clearly [ 16 ]. L SD is used here to solve the data imbalanced exists in the whole lower extremity MR images because the size of some venous thrombus is much smaller than background [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study

Sun

Xiong

Guan

et al. 2021

BioMed Research International

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Objective. Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. Methods. In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. Results. The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. Conclusions. The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images.

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“…The acceptance and rejection thresholds were 0.95 and 0.05, respectively, corresponding to the standard classification at the 95% confidence level. As an error function, cross-entropy (CE) was applied (Equation ( 2)) due to the fact that CE implementation allows interpreting output values as the probabilities of the object group membership [80].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

Świetlicka

Muszyński

Prein

et al. 2021

IJMS

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The potential of Fourier Transform infrared microspectroscopy (FTIR microspectroscopy) and multivariate analyses were applied for the classification of the frequency ranges responsible for the distribution changes of the main components of articular cartilage (AC) that occur during dietary β-hydroxy-β-methyl butyrate (HMB) supplementation. The FTIR imaging analysis of histological AC sections originating from 35-day old male piglets showed the change in the collagen and proteoglycan contents of the HMB-supplemented group compared to the control. The relative amount of collagen content in the superficial zone increased by more than 23% and in the middle zone by about 17%, while no changes in the deep zone were observed compared to the control group. Considering proteoglycans content, a significant increase was registered in the middle and deep zones, respectively; 62% and 52% compared to the control. AFM nanoindentation measurements collected from animals administered with HMB displayed an increase in AC tissue stiffness by detecting a higher value of Young’s modulus in all investigated AC zones. We demonstrated that principal component analysis and artificial neural networks could be trained with spectral information to distinguish AC histological sections and the group under study accurately. This work may support the use and effectiveness of FTIR imaging combined with multivariate analyses as a quantitative alternative to traditional collagenous tissue-related histology.

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MPCE: A Maximum Probability Based Cross Entropy Loss Function for Neural Network Classification

Cited by 69 publications

References 38 publications

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study

Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

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