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Hashimoto N, Hanada H, Miyoshi H, Nagaishi M, Sato K, Hontani H, Ohshima K, Takeuchi I. Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma. J Pathol Inform 2024; 15:100359. [PMID: 38322152 PMCID: PMC10844119 DOI: 10.1016/j.jpi.2023.100359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/07/2023] [Accepted: 12/23/2023] [Indexed: 02/08/2024] Open
Abstract
In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.
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Affiliation(s)
- Noriaki Hashimoto
- RIKEN Center for Advanced Intelligence Project, Furo-cho, Chikusa-ku, Nagoya, 4648603, Japan
| | - Hiroyuki Hanada
- RIKEN Center for Advanced Intelligence Project, Furo-cho, Chikusa-ku, Nagoya, 4648603, Japan
| | - Hiroaki Miyoshi
- Department of Pathology, Kurume University School of Medicine, 67 Asahi-machi, Kurume, 8300011, Japan
| | - Miharu Nagaishi
- Department of Pathology, Kurume University School of Medicine, 67 Asahi-machi, Kurume, 8300011, Japan
| | - Kensaku Sato
- Department of Pathology, Kurume University School of Medicine, 67 Asahi-machi, Kurume, 8300011, Japan
| | - Hidekata Hontani
- Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 4668555, Japan
| | - Koichi Ohshima
- Department of Pathology, Kurume University School of Medicine, 67 Asahi-machi, Kurume, 8300011, Japan
| | - Ichiro Takeuchi
- RIKEN Center for Advanced Intelligence Project, Furo-cho, Chikusa-ku, Nagoya, 4648603, Japan
- Department of Mechanical Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 4648603, Japan
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Xu X, Lubomski M, Holmes AJ, Sue CM, Davis RL, Muller S, Yang JYH. NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions. Microbiome 2023; 11:51. [PMID: 36918961 PMCID: PMC10015776 DOI: 10.1186/s40168-023-01475-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Unrevealing the interplay between diet, the microbiome, and the health state could enable the design of personalized intervention strategies and improve the health and well-being of individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. RESULTS We present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data-driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson's disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson's Disease but also for identifying diet-specific microbial signatures of disease. CONCLUSION In summary, NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases. Video Abstract.
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Affiliation(s)
- Xiangnan Xu
- Charles Perkins Centre, The University of Sydney, Camperdown, Sydney, NSW, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, Sydney, NSW, Australia
| | - Michal Lubomski
- Department of Neurology, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
- Department of Neurogenetics, Kolling Institute, Faculty of Medicine and Health, University of Sydney and Northern Sydney Local Health District, St Leonards, NSW, Australia
- The University of Notre Dame Australia, School of Medicine, Sydney, NSW, Australia
| | - Andrew J Holmes
- Charles Perkins Centre, The University of Sydney, Camperdown, Sydney, NSW, Australia
- School of Life and Environmental Sciences, University of Sydney, Camperdown, Sydney, NSW, Australia
| | - Carolyn M Sue
- Department of Neurology, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia
- Department of Neurogenetics, Kolling Institute, Faculty of Medicine and Health, University of Sydney and Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - Ryan L Davis
- Department of Neurogenetics, Kolling Institute, Faculty of Medicine and Health, University of Sydney and Northern Sydney Local Health District, St Leonards, NSW, Australia
| | - Samuel Muller
- School of Mathematics and Statistics, The University of Sydney, Camperdown, Sydney, NSW, Australia
- Department of Mathematics and Statistics, Macquarie University, Sydney, NSW, 2109, Australia
| | - Jean Y H Yang
- Charles Perkins Centre, The University of Sydney, Camperdown, Sydney, NSW, Australia.
- School of Mathematics and Statistics, The University of Sydney, Camperdown, Sydney, NSW, Australia.
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong, SAR, China.
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Aghdam MA, Sharifi A, Pedram MM. Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks. J Digit Imaging 2021; 32:899-918. [PMID: 30963340 DOI: 10.1007/s10278-019-00196-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.
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Affiliation(s)
- Maryam Akhavan Aghdam
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Sharifi
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran
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Trentin E, Lusnig L, Cavalli F. Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology. Neural Netw 2018; 97:137-51. [PMID: 29096202 DOI: 10.1016/j.neunet.2017.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 09/27/2017] [Accepted: 10/05/2017] [Indexed: 11/21/2022]
Abstract
A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too. Experiments are reported, involving (i) synthetic datasets, and (ii) a challenging sex determination task from 1400 scout-view CT-scan images of human crania. Incidentally, the empirical evidence entails also some conclusions of high anthropological relevance.
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Schaeffer MC, Aksenova T. Switching Markov decoders for asynchronous trajectory reconstruction from ECoG signals in monkeys for BCI applications. ACTA ACUST UNITED AC 2017; 110:348-360. [PMID: 28288824 DOI: 10.1016/j.jphysparis.2017.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 12/07/2016] [Accepted: 03/06/2017] [Indexed: 10/20/2022]
Abstract
Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article. Most discrete/continuous BCI hybrid decoders rely on static state models which don't exploit the dynamic of NC/IC state succession. A hybrid decoder, referred to as Markov Switching Linear Model (MSLM), is proposed in the present article. The MSLM assumes that the NC/IC state sequence is generated by a first-order Markov chain, and performs dynamic NC/IC state detection. Linear continuous movement models are probabilistically combined using the NC and IC state posterior probabilities yielded by the state decoder. The proposed decoder is evaluated for the task of asynchronous wrist position decoding from high dimensional space-time-frequency ElectroCorticoGraphic (ECoG) features in monkeys. The MSLM is compared with another dynamic hybrid decoder proposed in the literature, namely a Switching Kalman Filter (SKF). A comparison is additionally drawn with a Wiener filter decoder which infers NC states by thresholding trajectory estimates. The MSLM decoder is found to outperform both the SKF and the thresholded Wiener filter decoder in terms of False Positive Ratio and NC/IC state detection error. It additionally surpasses the SKF with respect to the Pearson Correlation Coefficient and Root Mean Squared Error between true and estimated continuous trajectories.
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Affiliation(s)
| | - Tetiana Aksenova
- Univ. Grenoble Alpes, CEA, LETI, CLINATEC, MINATEC Campus, 38000 Grenoble, France.
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Abstract
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification, and clustering. For regression and cluster analyses of continuous data, MoE usually uses normal experts following the Gaussian distribution. However, for a set of data containing a group or groups of observations with heavy tails or atypical observations, the use of normal experts is unsuitable and can unduly affect the fit of the MoE model. We introduce a robust MoE modeling using the t distribution. The proposed t MoE (TMoE) deals with these issues regarding heavy-tailed and noisy data. We develop a dedicated expectation-maximization (EM) algorithm to estimate the parameters of the proposed model by monotonically maximizing the observed data log-likelihood. We describe how the presented model can be used in prediction and in model-based clustering of regression data. The proposed model is validated on numerical experiments carried out on simulated data, which show the effectiveness and the robustness of the proposed model in terms of modeling non-linear regression functions as well as in model-based clustering. Then, it is applied to the real-world data of tone perception for musical data analysis, and the one of temperature anomalies for the analysis of climate change data. The obtained results show the usefulness of the TMoE model for practical applications.
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Affiliation(s)
- F Chamroukhi
- Université de Toulon, CNRS, LSIS, UMR 7296, 83957 La Garde, France; Aix Marseille Université, CNRS, ENSAM, LSIS, UMR 7296, 13397 Marseille, France; Laboratoire Paul Painlevé (LPP), UMR CNRS 8524, Université Lille 1, 59650 Villeneuve d'Ascq, France.
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Eavani H, Hsieh MK, An Y, Erus G, Beason-Held L, Resnick S, Davatzikos C. Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging. Neuroimage 2016; 125:498-514. [PMID: 26525656 PMCID: PMC5460911 DOI: 10.1016/j.neuroimage.2015.10.045] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 10/12/2015] [Accepted: 10/16/2015] [Indexed: 11/22/2022] Open
Abstract
In MRI studies, linear multi-variate methods are often employed to identify regions or connections that are affected due to disease or normal aging. Such linear models inherently assume that there is a single, homogeneous abnormality pattern that is present in all affected individuals. While kernel-based methods can implicitly model a non-linear effect, and therefore the heterogeneity in the affected group, extracting and interpreting information about affected regions is difficult. In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls. For this purpose, we use the Mixture-of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers. MOE approximates the non-linear boundary between the two groups with a piece-wise linear boundary, thus allowing discovery of multiple patterns of group differences. In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups. We validated our model using multiple simulation scenarios and performance measures. We applied this method to resting state functional MRI data from the Baltimore Longitudinal Study of Aging, to investigate heterogeneous effects of aging on brain function in cognitively normal older adults (>85years) relative to a reference group of normal young to middle-aged adults (<60years). We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging. While both older subgroups showed reduced functional connectivity in the Default Mode Network (DMN), increases in functional connectivity within the pre-frontal cortex as well as the bilateral insula were observed only for one of the two subgroups. Interestingly, the subgroup showing this increased connectivity (unlike the other subgroup) was, cognitively similar at baseline to the young and middle-aged subjects in two of seven cognitive domains, and had a faster rate of cognitive decline in one of seven domains. These results suggest that older individuals whose baseline cognitive performance is comparable to that of younger individuals recruit their "cognitive reserve" later in life, to compensate for reduced connectivity in other brain regions.
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Affiliation(s)
- Harini Eavani
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA.
| | - Meng Kang Hsieh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | - Yang An
- National Institute on Aging, Baltimore, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, USA
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Abstract
This article considers a methodology for flexibly characterizing the relationship between a response and multiple predictors. Goals are (1) to estimate the conditional response distribution addressing the distributional changes across the predictor space, and (2) to identify important predictors for the response distribution change both within local regions and globally. We first introduce the probit stick-breaking process (PSBP) as a prior for an uncountable collection of predictor-dependent random distributions and propose a PSBP mixture (PSBPM) of normal regressions for modeling the conditional distributions. A global variable selection structure is incorporated to discard unimportant predictors, while allowing estimation of posterior inclusion probabilities. Local variable selection is conducted relying on the conditional distribution estimates at different predictor points. An efficient stochastic search sampling algorithm is proposed for posterior computation. The methods are illustrated through simulation and applied to an epidemiologic study.
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Affiliation(s)
- Yeonseung Chung
- Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave. SPH2, 4th Floor, Boston, MA 02115 ()
| | - David B. Dunson
- Department of Statistical Science, Duke University, 218 Old Chemistry Building, Box 90251, Durham, NC 27707 ()
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