6 results on '"Suggestive annotation"'
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2. Suggestive Annotation of Brain Tumour Images with Gradient-Guided Sampling
- Author
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Dai, Chengliang, Wang, Shuo, Mo, Yuanhan, Zhou, Kaichen, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor
- Published
- 2020
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3. Weakly supervised vessel segmentation in X-ray angiograms by self-paced learning from noisy labels with suggestive annotation.
- Author
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Zhang, Jingyang, Wang, Guotai, Xie, Hongzhi, Zhang, Shuyang, Huang, Ning, Zhang, Shaoting, and Gu, Lixu
- Subjects
- *
CONVOLUTIONAL neural networks , *SUPERVISED learning , *ANNOTATIONS , *CORONARY arteries , *LABELS - Abstract
The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees. To alleviate the burden on the annotator, we propose a novel weakly supervised training framework that learns from noisy pseudo labels generated from automatic vessel enhancement, rather than accurate labels obtained by fully manual annotation. A typical self-paced learning scheme is used to make the training process robust against label noise while challenged by the systematic biases in pseudo labels, thus leading to the decreased performance of CNNs at test time. To solve this problem, we propose an annotation-refining self-paced learning framework (AR-SPL) to correct the potential errors using suggestive annotation. An elaborate model-vesselness uncertainty estimation is also proposed to enable the minimal annotation cost for suggestive annotation, based on not only the CNNs in training but also the geometric features of coronary arteries derived directly from raw data. Experiments show that our proposed framework achieves 1) comparable accuracy to fully supervised learning, which also significantly outperforms other weakly supervised learning frameworks; 2) largely reduced annotation cost, i.e., 75.18% of annotation time is saved, and only 3.46% of image regions are required to be annotated; and 3) an efficient intervention process, leading to superior performance with even fewer manual interactions. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
4. Suggestive annotation of brain MR images with gradient-guided sampling
- Abstract
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.
- Published
- 2022
5. Suggestive annotation of brain MR images with gradient-guided sampling
- Author
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Dai, Chengliang, Wang, Shuo, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, and National Institutes of Health
- Subjects
FOS: Computer and information sciences ,Diagnostic Imaging ,Technology ,Active learning ,Computer Science - Artificial Intelligence ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Health Informatics ,Computer Science, Artificial Intelligence ,09 Engineering ,Machine Learning ,Engineering ,Image Processing, Computer-Assisted ,Humans ,Radiology, Nuclear Medicine and imaging ,Engineering, Biomedical ,11 Medical and Health Sciences ,Image segmentation ,Science & Technology ,Radiological and Ultrasound Technology ,Brain Neoplasms ,Radiology, Nuclear Medicine & Medical Imaging ,Brain ,Magnetic Resonance Imaging ,Computer Graphics and Computer-Aided Design ,Nuclear Medicine & Medical Imaging ,Artificial Intelligence (cs.AI) ,Brain MRI ,Computer Science ,Suggestive annotation ,Computer Science, Interdisciplinary Applications ,Computer Vision and Pattern Recognition ,Life Sciences & Biomedicine - Abstract
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications., Comment: Manuscript accepted by MedIA
- Published
- 2022
6. Suggestive Annotation of Brain Tumour Images with Gradient-Guided Sampling
- Abstract
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain tumour images that is able to suggest informative sample images for human experts to annotate. Our experiments show that training a segmentation model with only 19% suggestively annotated patient scans from BraTS 2019 dataset can achieve a comparable performance to training a model on the full dataset for whole tumour segmentation task. It demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.
- Published
- 2020
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