Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network

Xu, Qiang; Zeng, Yu; Tang, Wenjun; Peng, Wei; Xia, Tingwei; Li, Zongrun; Teng, Fei; Li, Weihong; Guo, Jinhong

doi:10.1109/jbhi.2020.2986376

Cited by 152 publications

(80 citation statements)

References 35 publications

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“…A multiparametric MRI was performed prior to stage Tl renal tumour puncture using a Siemens 3.0 T MRI scanners with an abdominal phased array coil to receive the signal. Diffusion imaging axial scans were performed using a single excitation planar echo (EPI) sequence, and the magnetic resonance scanner automatically calculated ADC images, exported 1 mm thick thin DICOM data, and applied RadiAnt DICOM Viewer V4.6.5 to obscure basic patient information for post-automated segmentation [27][28][29][30].…”

Section: Image Acquisition and Parametersmentioning

confidence: 99%

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

Sun

Chang

et al. 2022

Journal of Healthcare Engineering

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We evaluate the stability of the clinical application of the MAP scoring system based on anatomical features of renal tumour images, explore the relevance of this scoring system to the choice of surgical procedure for patients with limited renal tumours, and investigate the effectiveness of automated segmentation and reconstruction 3D models of renal tumour images based on U-net for interpretative cognitive navigation during laparoscopy Tl stage radical renal tumour cancer surgery. A total of 5 000 kidney tumour images containing manual annotations were applied to the training set, and a stable and efficient full CNN algorithm model oriented to clinical needs was constructed to regionalism and multistructure and to finely automate segmentation of kidney tumour images, output modelling information in STL format, and apply a tablet computer to intraoperatively display the Tl stage kidney tumour model for cognitive navigation. Based on a training sample of MR images from 201 patients with stage Tl renal tumour cancer, an adaptation of the classical U-net allows individual segmentation of important structures such as renal tumours and 3D visualisation to visualise the structural relationships and the extent of tumour invasion at key surgical sites. The preoperative CT and clinical data of 225 patients with limited renal tumours treated surgically at our hospital from August 2011 to August 2012 were retrospectively analysed by three imaging physicians using the MAP scoring system for the total score and the variables R (maximum diameter), E (exogenous/endogenous), N (distance from the renal sinus), A (ventral/dorsal), L (relationship along the longitudinal axis of the kidney), and h (whether in contact with the renal hilum). The score for each variable (contact with the renal hilum) was statistically compared with each other for the three observers. Patients were divided into three groups according to the total score—low, medium, and high—and according to the surgical procedure—radical and partial resection. The correlation between the total score and the score of each variable and the choice of surgical procedure was analysed. The agreement rate of the total score and the score of each variable for all three observers was over 90% ( P ≤ 0.001). The map scoring system based on the anatomical features of renal tumour imaging was well stabilized, and the scores were significantly correlated with the surgical approach.

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Section: Image Acquisition and Parametersmentioning

confidence: 99%

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

Sun

Chang

et al. 2022

Journal of Healthcare Engineering

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“…The study showed that the satisfaction rate of postoperative nursing in the intervention group was significantly higher than that in the control group ( P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group ( P < 0.05). The total satisfaction of postoperative nursing in the intervention group was higher than that in the control group ( P < 0.05) [ 36 , 37 ]. After the intervention, the proportion of the patients with the affected limb function returning to normal in the intervention group was significantly higher than that in the control group ( P < 0.05), and the proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group was significantly lower than that in the control group ( P < 0.05) [ 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

He¹,

Zhang²,

Shen³

2022

Journal of Healthcare Engineering

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This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients’ compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group ( P < 0.05 ). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment ( P < 0.05 ), and the scores of the quality of life in the intervention group were higher than those in the control group ( P < 0.05 ). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group ( P < 0.05 ). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group ( P < 0.001 ), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group ( P < 0.05 ).

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“…In particular, thresholding, which is commonly used in gray surface images, is a simple method. In the medical industry, optimization, classification, and diagnosis are very common, using imaging equipment ([ 44 – 46 ]). In general, in this regard, thresholding methods are very efficient.…”

Section: Traditional Methodsmentioning

confidence: 99%

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

Valizadeh

Shariatee

2021

Computational Intelligence and Neuroscience

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Image medical semantic segmentation has been employed in various areas, including medical imaging, computer vision, and intelligent transportation. In this study, the method of semantic segmenting images is split into two sections: the method of the deep neural network and previous traditional method. The traditional method and the published dataset for segmentation are reviewed in the first step. The presented aspects, including all-convolution network, sampling methods, FCN connector with CRF methods, extended convolutional neural network methods, improvements in network structure, pyramid methods, multistage and multifeature methods, supervised methods, semiregulatory methods, and nonregulatory methods, are then thoroughly explored in current methods based on the deep neural network. Finally, a general conclusion on the use of developed advances based on deep neural network concepts in semantic segmentation is presented.

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Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network

Cited by 152 publications

References 35 publications

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

The Progress of Medical Image Semantic Segmentation Methods for Application in COVID‐19 Detection

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