Multi-Modal Fusion Learning For Cervical Dysplasia Diagnosis

Chen, Tingting; Ma, Xinjun; Ying, Xingde; Wang, Wenzhe; Yuan, Chunnv; Lü, Weiguo; Chen, Danny Z.; Wu, Jian

doi:10.1109/isbi.2019.8759303

Cited by 24 publications

(9 citation statements)

References 13 publications

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“…The strategy uses the Laplacian Pyramid to reconstruct the image in the fusion process after generating a weighted map of the source image using the deep network. Chen et al [19] defined the Laplace pyramid to describe the lost high-frequency detail information caused by the convolution and down-sampling operations in the Gaussian Pyramid (GP) method.…”

Section: Laplacian Pyramid Methodsmentioning

confidence: 99%

“…This enabled us to reduce the computational time with improved accuracy in complicated scenarios while eliminating the need for post-processing tasks and activities. The lung image fusion method is based on the LP and ASR [19] methods of image fusion, resulting in a better outcome and a better method of medical image fusion in the treatment of lung cancer [20]. We used LP decomposition for the multi-view clinical CT images to increase the speed of constructing the sub-dictionaries using the ASR method.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

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Section: Laplacian Pyramid Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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“…The model attains a detection accuracy of 0.68 in terms of the intersection of union (IoU) measure. T. Chen et al [32] used multimodality for the diagnosis of cervical dysplasia and attain an accuracy of 87.4% (88.6% sensitivity and 86.1% specificity). P. Guo et al [33] worked on 30,000 smartphone-captured images and used ensembles deep learning to classify images into the cervix or non-cervix which achieve an accuracy of 91.6%.…”

Section: Comparisons With Existing Methodologiesmentioning

confidence: 99%

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

Dhawan¹,

Singh²,

Arora³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images. OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3). METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images. RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset. CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.

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“…The main advantage of decision-level fusion strategy comes from the ability to learn the complementary information from the different modalities independently since the latent information in different modalities can be diverse. The works of [20], [21], [63] developed multi-modal diagnosis algorithms by leveraging the representations from both structured and unstructured data.…”

Section: Multi-modal Machine Learningmentioning

confidence: 99%

A Novel Attention-Based Multi-Modal Modeling Technique on Mixed Type Data for Improving TFT-LCD Repair Process

Lai

2022

IEEE Access

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In the Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) manufacturing, conducting a machine learning based system with multiple data types has become urgently desired to solve complicated problems. This paper proposes a novel deep learning approach: TabVisionNet, which is modeled by utilizing the information from both tabular data and image data. Tabular data and image data are first encoded by a Tabular encoder and a CNN encoder respectively, then a novel attention mechanism called Sequential Decision Attention was integrated into the framework, that combines the information from two modalities. This novel approach can capture the complex relationship between modalities then gain better generalization and faster convergence in the training process. Conducting an experiment, the performance of our novel approach was significantly better than single-modal and other multi-modal learning approaches in our real case scenario.

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Multi-Modal Fusion Learning For Cervical Dysplasia Diagnosis

Cited by 24 publications

References 13 publications

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Cervix Image Classification for Prognosis of Cervical Cancer using Deep Neural Network with Transfer Learning

A Novel Attention-Based Multi-Modal Modeling Technique on Mixed Type Data for Improving TFT-LCD Repair Process

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