Deep Learning Approach for Medical Image Analysis

Adegun, Adekanmi Adeyinka; Viriri, Serestina; Ogundokun, Roseline Oluwaseun

doi:10.1155/2021/6215281

Cited by 37 publications

(19 citation statements)

References 23 publications

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“…Background. In medical imaging, the use of deep learning for automatic diagnosis has become an unstoppable power [1,2]. However, it is limited by factors such as data acquisition channels, and most of the existing researches focus on single-modality images.…”

Section: Introductionmentioning

confidence: 99%

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Chi

Wang

et al. 2022

Journal of Healthcare Engineering

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Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex communication with other functional areas. Its importance to the human body is self-evident. There are also many scholars studying the mechanism of apoptosis. This paper proposes a study of neuronal apoptosis in patients with liver cirrhosis and neuronal epileptiform discharge models based on multi-modal fusion deep learning, aiming to study the influencing factors of abnormal neuronal discharge in the brain. The method in this paper is to study multi-modal information fusion methods, perform Bayesian inference, and analyze multi-modal medical data. The function of these research methods is to obtain the relationship between the independence of information and the intersection of information among modalities. In the neuronal epileptiform discharge model, the mRNA expression level of the necroptotic signaling pathway related protein was detected, and the mechanism of neuronal necrosis in patients with liver cirrhosis was explored. Experiments show that the neuron recognition rate has been increased from 67.2% to 84.5%, and the time has been reduced, proving the effectiveness of deep learning.

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Section: Introductionmentioning

confidence: 99%

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Chi

Wang

et al. 2022

Journal of Healthcare Engineering

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“…The discriminator in our model has an unlabeled data loss, labeled data loss, and refined prediction loss. The overall loss function is computed as follows: l discriminator = λ labeled l labeled +λ unlabeled l unlabeled +λ fake l fake (9) Where λ labeled , λ unlabeled , and λ fake are hyper-parameters. We set the hyper-parameters in Equation 9 to λ labeled = 1.0, λ unlabeled = 1.0andλ fake = 2.0.…”

Section: B Loss Function 1) Discriminator Loss Functionmentioning

confidence: 99%

“…However, training deep learning models requires large sets of labeled images [6]. Due to the limited sets of data in medical applications [7], [8], semi-supervised learning techniques has been used to address this issue by means of unlabeled image [9], [10]. Segmentation results can be improved by adopting unlabeled images [11] or images with weak annotation, such as image level tags [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

2022

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Image segmentation is a challenging problem in medical applications. Medical imaging has become an integral part of machine learning research, as it enables inspecting interior human body with no surgical intervention. Much research has been conducted to study brain segmentation. However, prior studies usually employ one-stage models to segment brain tissues, which could lead to a significant information loss. In this paper, we propose a multi-stage Generative Adversarial Network (GAN ) model to resolve existing issues of one-stage models. To do this, we apply a coarse-to-fine method to improve brain segmentation using a multi-stage GAN . In the first stage, our model generates a coarse outline for both the background and brain tissues. Then, in the second stage, the model generates a refine outline for the white matter (W M ), gray matter (GM ), and cerebrospinal fluid (CSF ). We perform a fusion of the coarse and refine outlines to achieve high results. Despite using very limited data, we obtain an improved Dice Coefficient (DC) accuracy of up to 5% compared to one-stage models. We conclude that our model is more efficient and accurate in practice for brain segmentation of both infants and adults. In addition, we observe that our multi-stage model is 2.69−13.93 minutes faster than prior models. Moreover, our multi-stage model achieves higher performance with only a few-shot learning, in which only limited labeled data is available. Therefore, for medical images, our solution is applicable to a wide range of image segmentation applications for which convolution neural networks and one-stage methods have failed. This helps to advance the process of analyzing brain images, thus providing many advantages to the healthcare system, especially in critical health situations where urgent intervention is needed.

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“…Recently, many deep learning methods have been applied to dynamic hand gesture recognition in an end-to-end manner [ 9 , 10 ]. Unlike image classification task, dynamic hand gesture recognition, a task for the video, needs to consider not only the spatial information of each frame in a video sequence but also the temporal correlation between frames, which brings great challenges to hand gesture recognition task.…”

Section: Related Workmentioning

confidence: 99%

Attentive 3D‐Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer

Liu

Kong

et al. 2021

Computational Intelligence and Neuroscience

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Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB-D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB-D data is available for training so as to attain the complementary information. Fortunately, a kind of method via multimodal training and unimodal testing has been proposed. However, unimodal feature representation and cross-modality transfer still need to be further improved. To this end, this paper proposes a new 3D-Ghost and Spatial Attention Inflated 3D ConvNet (3DGSAI) to extract high-quality features for each modality. The baseline of 3DGSAI network is Inflated 3D ConvNet (I3D), and two main improvements are proposed. One is 3D-Ghost module, and the other is the spatial attention mechanism. The 3D-Ghost module can extract richer features for hand gesture representation, and the spatial attention mechanism makes the network pay more attention to hand region. This paper also proposes an adaptive parameter for positive knowledge transfer, which ensures that the transfer always occurs from the strong modality network to the weak one. Extensive experiments on SKIG, VIVA, and NVGesture datasets demonstrate that our method is competitive with the state of the art. Especially, the performance of our method reaches 97.87% on the SKIG dataset using only RGB, which is the current best result.

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Deep Learning Approach for Medical Image Analysis

Cited by 37 publications

References 23 publications

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

Attentive 3D‐Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer

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