Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

Guo, Jirui; Cao, Wuteng; Nie, Bairun; Qin, Qiyuan

doi:10.1109/jtehm.2022.3224021

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…And to our best knowledge, the performance of the CNN model was always relevant to the size of the training set. So in the future, some semi-supervised learning (SSL) (Wan et al 2016) or even unsupervised learning (Guo et al 2023) strategies will be attempted into the CAD method on the purpose of improving the behavior of our CAD method without increasing the number of labels.…”

Section: Discussionmentioning

confidence: 99%

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Zhang,

Wang,

Zhao

et al. 2023

Phys. Med. Biol.

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Objective:There has been a considerable amount of Computer-Aided Diagnosis (CAD) methods highlighted in the field of ultrasonic examination (USE) of thyroid nodules. However, few researches focused on the automatic risk classification, which was the basis for determining whether Fine Needle Aspiration (FNA) was needed. The aim of this work was to implement automatic risk level assessment of thyroid nodules.Approach:Firstly, 1862 cases of thyroid nodules with the results of USE and FNA were collected as the dataset. Then, an improved U-Net++ model was utilized for segmenting thyroid nodules in ultrasound images automatically. Finally, the segmentation result was imported into a multi-task convolutional neural network (MT-CNN), the design of which was based on the clinical guideline called KWAK TI-RADS. Apart from the category of benign and malignant, the MT-CNN also exported the classification result of four malignant features, Solid Component (SC), Hypoechogenicity or Marked Hypoechogenicity (HMH), Microlobulated or Irregular Margin (MIM), Microcalcification (MC), which were used for counting the risk level in KWAK TI-RADS.Main results:The performance of the improved U-Net++ was evaluated on our test set, including 302 cases. The Dice coefficient and Intersection over Union (IoU) reached 0.899, 0.816, respectively. The classification accuracy rates of SC, HMH, MIM, MC, were 94.5%, 92.8%, 86.1%, 88.9%, while the false positive (FP) rate was 6.0%, 5.6%, 10.6%, 12.9% separately. As for the category of benign and malignant, the precision and recall rates were 93.7% and 94.4%.Significance:The proposed CAD method showed favourable performance in the diagnosis of thyroid nodules. Compared with other methods, it could provide reports closer to clinical practice for doctors.

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Section: Discussionmentioning

confidence: 99%

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Zhang,

Wang,

Zhao

et al. 2023

Phys. Med. Biol.

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“…City80K is a data set that simulates the movement trajectory of 80,000 pedestrians 24 h a day in a metropolis with 26 plates. It contains five sensitive attribute values, one of which is chosen as the sensitive value in the experiment (Guo et al, 2023). All comparison algorithms are implemented in MATLAB language and run on a workstation with Intel i7-5500U CPU (3.0 GHz), 8 GB memory and 7200 RPM 1 TB hard disk.…”

Section: Experimental Environment and Data Setmentioning

confidence: 99%

New algorithm to ensure virtual simulation data security based on deep learning using applied innovation design

Bo,

Bexci

2023

Int. J. Appl. Sci. Eng.

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The virtual simulation laboratory saves the material resources and equipment investment required for simulating a real experiment environment. This paves the learners to experiment and explore in a virtual environment, reducing resource waste and cost. In addition, the virtual simulation laboratory can also realize the sharing of resources, and academic institutions can share the platform and content of the virtual laboratory to improve the efficiency of resource utilization. But the virtual simulation experiment data can be is easily hacked from the network, hence making it challenging task to study virtual simulation data security. In this paper, we research the virtual simulation data security based on deep learning through applied innovation design and proposed a new algorithm. The minimum violation sequence set in the virtual simulation data set is identified and the suppression mode of the minimum violation sequence is judged. The score table is constructed for the instances in the sequence, and the corresponding instances are selected and suppressed according to the score value. The cross-attention module of Transformer Structure is proposed to aggregate the global and local feature information between left and right graphs and obtain the long-distance dependence relationship between left and right graphs along the polar direction, which can more effectively fuse the global feature information of left and right graphs. The results show that the proposed algorithm can not only ensure the safety of trajectory data but also improve the availability of data.

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“…Kecerdasan buatan atau yang biasa disebut artificial intelligence (AI) adalah teknologi komputer yang dirancang untuk meniru kecerdasan manusia dan berfikir secara rasional dalam belajar dan memecahkan masalah. Machine learning (ML) dan deep learning (DL) adalah bidang AI (Prasetiyo et al, 2020) yang semakin banyak digunakan untuk menganalisis maupun mengklasifikasikan data medis (Guo et al, 2023). Dengan kemajuan teknologi komputasi, DL menjadi semakin penting dalam bidang pengenalan pola.…”

Section: Pendahuluanunclassified

Klasifikasi Diabetic Retinopathy Menggunakan CNN dengan Arsitektur yang Dimodifikasi

Wibowo,

Alamsyah

2023

ijmns

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Diabetic Retinopathy (DR), juga dikenal sebagai penyakit mata diabetes, terjadi karena kerusakan pada retina akibat diabetes melitus yang berkepanjangan. DR dapat diklasifikasikan melalui gambar fundus retina. Namun, klasifikasi retinopati diabetik membutuhkan waktu yang tidak sedikit dan belum ada model yang memiliki performa tinggi dalam mengklasifikasikan DR. Tujuan dari penelitian ini mengusulkan model yang diharapkan memiliki tingkat akurasi yang lebih tinggi daripada penelitian sebelumnya. Metode yang digunakan adalah Convolutional Neural Network (CNN). Hasil evaluasi menunjukkan klasifikasi DR menggunakan CNN berarsitektur VGG-16 Net menghasilkan akurasi 96,24% sedangkan klasifikasi DR menggunakan CNN berarsitektur yang dimodifikasi menghasilkan akurasi 96.45%. Pada penelitian ini, akurasi model klasifikasi dapat ditingkatkan dengan menggunakan metode CNN berarsitektur termodifikasi.

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Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

Cited by 11 publications

References 25 publications

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

New algorithm to ensure virtual simulation data security based on deep learning using applied innovation design

Klasifikasi Diabetic Retinopathy Menggunakan CNN dengan Arsitektur yang Dimodifikasi

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