Lung Nodule Detection With Deep Learning in 3D Thoracic MR Images

Li, Yanfeng; Zhang, Linlin; Chen, Houjin; Yang, Na

doi:10.1109/access.2019.2905574

Cited by 40 publications

(19 citation statements)

References 21 publications

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“…Similarly, Bouget et al proposed a combination of Mask R-CNN and U-Net for the segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging [219]. Li et al proposed a lung nodule detection method based on Faster R-CNN for thoracic MRI in a transfer learning manner [220]. A false positive (FP) reduction scheme based on anatomical characteristics is designed to reduce the FPs and preserve the true nodule.…”

Section: Overview Of Workmentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

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Section: Overview Of Workmentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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“…This diagnosis technique is based on captured images of several parts of the human body and a computer-based program for identifying abnormal signs in these parts in lieu of the professional knowledge of doctors. This technique has been successful in many applications, such as brain [6,7,8,9,10], breast [11,12,13,14,15,16,17], lung [18,19,20], and thyroid [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42] nodule detection/classification problems. For diagnosing thyroid nodules, previous studies focused on designing computer-based systems to perform several functions to detect and/or classify thyroid images into several classes such as nodule versus non-nodule; benign versus malign; follicles versus fibrosis, etc.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Nguyen

Pham

Batchuluun

et al. 2019

JCM

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Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

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“…Li et al [9] proposed pulmonary nodule recognition from thoracic MR images. In this technique, a faster Residual-CNN was designed by using the optimized parameters, a spatial 3channel input structure, and transfer learning for finding the lung nodule regions.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Nagarajan¹,

Tajunisha²

2021

RIA

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The classification of ovarian cancer types is a very challenging process for physicians' eyes. To solve this problem, this article proposes a new deep learner, which classifies ovarian cancer types from Computerized Tomography (CT) images. Firstly, a Deep Convolutional Neural Network (DCNN) model depending on AlexNet is proposed to categorize ovarian cancer from CT images. But its efficiency is not satisfactorily high. So, DCNN is built based on the fusion of AlexNet, VGG, and GoogLeNet. The fusion is carried out at the SoftMax layer by fusing the SoftMax values of each network structure using a weighted sum to obtain the overall classification outcome. But overfitting problems can occur due to an inadequate number of training images. Thus, a Deep Semi-Supervised Generative Learning with DCNN model (DSSGL-DCNN) is proposed by using a Generative Adversarial Network (GAN) which augments the training samples to solve the overfitting problem. Once the augmented dataset is obtained, the fused DCNN model is learned to classify ovarian cancer types. Further, the classified outcomes can be used as a useful guideline for physicians in medical diagnosis. Finally, the experimental results show that the DSSGL-DCNN achieves higher efficiency compared to the other DCNN architectures.

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Lung Nodule Detection With Deep Learning in 3D Thoracic MR Images

Cited by 40 publications

References 21 publications

A review of deep learning based methods for medical image multi-organ segmentation

A review of deep learning based methods for medical image multi-organ segmentation

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

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