Dataset Information

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network.

ABSTRACT: In the medical field, various studies using artificial intelligence (AI) techniques have been attempted. Numerous attempts have been made to diagnose and classify diseases using image data. However, different forms of fracture exist, and inaccurate results have been confirmed depending on condition at the time of imaging, which is problematic. To overcome this limitation, we present an encoder-decoder structured neural network that utilizes radiology reports as ancillary information at training. This is a type of meta-learning method used to generate sufficiently adequate features for classification. The proposed model learns representation for classification from X-ray images and radiology reports simultaneously. When using a dataset of only 459 cases for algorithm training, the model achieved a favorable performance in a test dataset containing 227 cases (classification accuracy of 86.78% and classification F1 score of 0.867 for fracture or normal classification). This finding demonstrates the potential for deep learning to improve performance and accelerate application of AI in clinical practice.

SUBMITTER: Lee C

PROVIDER: S-EPMC7426947 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network.

Lee Changhwan C Jang Jongseong J Lee Seunghun S Kim Young Soo YS Jo Hang Joon HJ Kim Yeesuk Y

Scientific reports 20200813 1

In the medical field, various studies using artificial intelligence (AI) techniques have been attempted. Numerous attempts have been made to diagnose and classify diseases using image data. However, different forms of fracture exist, and inaccurate results have been confirmed depending on condition at the time of imaging, which is problematic. To overcome this limitation, we present an encoder-decoder structured neural network that utilizes radiology reports as ancillary information at training. ...[more]

PMID: 32792627

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Project description:Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.

Dataset Information

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network.

Publications

Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network.

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