Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model

Arya, Nikhilanand; Saha, Sriparna

doi:10.1109/tcbb.2020.3018467

Cited by 73 publications

(42 citation statements)

References 32 publications

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“…Following (Sun et al, 2007 ; Arya and Saha, 2020 ), we adopted AUC as the evaluation metric, which is widely used in survival prediction tasks. We plotted the receiver operating characteristic (ROC) curve to show the interaction between true positive (TP) and false positive (FP).…”

Section: Resultsmentioning

confidence: 99%

“…In order to verify the effect of MAFN, we compared the results of our method with three existing deep learning-based methods, including STACKED_RF (Arya and Saha, 2020 ), AMND (Chen et al, 2019 ), and MDNNMD (Sun et al, 2018 ). Experiments were conducted on METABRIC, TCGA- BRCA, GSE8757, and GSE69035 dataset, and the ROC curves of different methods are plotted in Figure 6 .…”

Section: Resultsmentioning

confidence: 99%

“…The experimental results show that MAFN performs better compared with existing research methods to the best of our knowledge (Jefferson et al, 1997;Xu et al, 2012;Nguyen et al, 2013;Sun et al, 2018;Chen et al, 2019;Arya and Saha, 2020).…”

Section: Introductionmentioning

confidence: 88%

“…In order to verify the effect of MAFN, we compared the results of our method with three existing deep learning-based methods, including STACKED_RF (Arya and Saha, 2020), AMND (Chen et al, 2019), and MDNNMD (Sun et al, 2018 To further evaluate the performance of MAFN, we also compared it with three widely used traditional classification methods, including LR (Jefferson et al, 1997), RF (Nguyen et al, 2013), and SVM (Xu et al, 2012). Experiments were conducted on METABRIC and TCGA-BRCA dataset.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Cheerla and Gevaert ( 2019 ) proposed an unsupervised encoder to compress clinical data, mRNA expression data, microRNA expression data, and histopathology whole slide images (WSI) into single feature vectors for each patient; these feature vectors were then aggregated to predict patient survival. Nikhilanand et al (Arya and Saha, 2020 ) introduced a STACKED_RF method based on a stacked integrated framework combined with random forest in multimodal data. These results show that better performances can be achieved with multimodal data.…”

Section: Introductionmentioning

confidence: 99%

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A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

et al. 2021

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Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 88%

“…In order to verify the effect of MAFN, we compared the results of our method with three existing deep learning-based methods, including STACKED_RF (Arya and Saha, 2020), AMND (Chen et al, 2019), and MDNNMD (Sun et al, 2018 To further evaluate the performance of MAFN, we also compared it with three widely used traditional classification methods, including LR (Jefferson et al, 1997), RF (Nguyen et al, 2013), and SVM (Xu et al, 2012). Experiments were conducted on METABRIC and TCGA-BRCA dataset.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

et al. 2021

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Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection

Abhisheka,

Biswas,

Purkayastha

2024

Int J Imaging Syst Tech

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Breast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention‐based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA‐ENet) designed for early‐stage breast cancer detection that leverages the ability of ensemble technique over single classifier‐based models for more robust and accurate prediction. The proposed model employs a weighted average‐based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over‐sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets.

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Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion

Chharia

Kumar

2021

Lecture Notes in Computer Science

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Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model

Cited by 73 publications

References 32 publications

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection

Foreseeing Survival Through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion

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