Visual saliency-based active learning for prostate magnetic resonance imaging segmentation

Mahapatra, Dwarikanath; Buhmann, Joachim M.

doi:10.1117/1.jmi.3.1.014003

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…Among works involving mpMRI, Mahapatra and Buhmann [26] developed an active learning-based method for prostate MRI segmentation using visual saliency cues. The method achieved an average DSC of 0.807.…”

Section: Related Workmentioning

confidence: 99%

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Gavade¹,

Nerli²,

Kanwal

et al. 2023

Computers

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Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved into a significant imaging modality in this regard, which provides detailed images of the anatomy and tissue characteristics of the prostate gland. However, interpreting mpMRI images can be challenging for humans due to the wide range of appearances and features of PCa, which can be subtle and difficult to distinguish from normal prostate tissue. Deep learning (DL) approaches can be beneficial in this regard by automatically differentiating relevant features and providing an automated diagnosis of PCa. DL models can assist the existing clinical decision support system by saving a physician’s time in localizing regions of interest (ROIs) and help in providing better patient care. In this paper, contemporary DL models are used to create a pipeline for the segmentation and classification of mpMRI images. Our DL approach follows two steps: a U-Net architecture for segmenting ROI in the first stage and a long short-term memory (LSTM) network for classifying the ROI as either cancerous or non-cancerous. We trained our DL models on the I2CVB (Initiative for Collaborative Computer Vision Benchmarking) dataset and conducted a thorough comparison with our experimental setup. Our proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature. Results demonstrate that the proposed approach can detect PCa disease with high precision and also has a high potential to improve clinical assessment.

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Section: Related Workmentioning

confidence: 99%

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Gavade¹,

Nerli²,

Kanwal

et al. 2023

Computers

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“…The proposed learning framework enforced a set of specifically designed diversity constraints for the histopathological image annotation task. The visual saliency of objects 337 inside an image was considered as a measure for selecting samples. The similarities between labeled and unlabeled data were computed and encoded in a graph.…”

Section: C Data Annotation Via Active Learningmentioning

confidence: 99%

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

Shehata

Khalifa

Soliman

et al. 2019

IEEE Trans. Biomed. Eng.

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“…Recently, a lot of image segmentation methods using AL strategies have been proposed. In the vast majority of cases, active learners use uncertainty sampling strategies [20] to select unlabeled data which contain signi cant information to be labeled by oracle [21][22][23][24]. e key point of uncertainty sampling strategy is to measure the uncertainty of data.…”

Section: E Application Of Deep Activementioning

confidence: 99%

Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

Zhao

Han

Jia

et al. 2018

Journal of Electrical and Computer Engineering

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Deep learning technique has made a tremendous impact on medical image processing and analysis. Typically, the procedure of medical image processing and analysis via deep learning technique includes image segmentation, image enhancement, and classification or regression. A challenge for supervised deep learning frequently mentioned is the lack of annotated training data. In this paper, we aim to address the problems of training transferred deep neural networks with limited amount of annotated data. We proposed a versatile framework for medical image processing and analysis via deep active learning technique. The framework includes (1) applying deep active learning approach to segment specific regions of interest (RoIs) from raw medical image by using annotated data as few as possible; (2) generative adversarial Network is employed to enhance contrast, sharpness, and brightness of segmented RoIs; (3) Paced Transfer Learning (PTL) strategy which means fine-tuning layers in deep neural networks from top to bottom step by step to perform medical image classification or regression tasks. In addition, in order to understand the necessity of deep-learning-based medical image processing tasks and provide clues for clinical usage, class active map (CAM) is employed in our framework to visualize the feature maps. To illustrate the effectiveness of the proposed framework, we apply our framework to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task.

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Visual saliency-based active learning for prostate magnetic resonance imaging segmentation

Cited by 12 publications

References 28 publications

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

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