@article{scholars13914,
             doi = {10.1007/978-981-15-4015-8{$_1$}{$_5$}},
           pages = {174--182},
           title = {Automatic report generation for chest X-Ray images: A multilevel multi-attention approach},
            note = {cited By 7; Conference of 4th International Conference on Computer Vision and Image Processing, CVIP 2019 ; Conference Date: 27 September 2019 Through 29 September 2019; Conference Code:238879},
            year = {2020},
         journal = {Communications in Computer and Information Science},
          volume = {1147 C},
       publisher = {Springer},
        abstract = {A comprehensive X-ray imaging report greatly assists the medical professional to investigate an indispensable condition and medication. The preparation of an extensive and diversified medical report by analysing the chest X-ray image is a time-consuming task and requires highly experienced professionals. This work targets the fundamental problem of generating a long and multifarious medical report for the chest X-ray image. It introduces a novel Multilevel Multi-Attention based encoder-decoder approach by combining Context Level Visual Attention and Textual Attention to generate a plausible medical report for different views of chest X-ray images. It exploited the proven ability of the Convolutional Neural Network to acquire course information of visual-spatial regions as an encoder. It leverages the strength of the Long Short-Term Memory network to learn long sequential dependencies and the ability of attention to focus on the prominent section as a decoder. The proposed method emphasizes on contextual coherence in intra and inter-sentence dependency within a report to improve the overall medical report generation quality. The effectiveness of the proposed model is evaluated on the publicly available IU chest X-ray dataset consisting of chest images along with multifarious radiology reports. The final performance of the proposed model is reported using the COCO-caption evaluation API. It shows a significant improvement in a medical report generation task compared to state-of-the-art methods. {\^A}{\copyright} Springer Nature Singapore Pte Ltd 2020.},
          author = {Gajbhiye, G. O. and Nandedkar, A. V. and Faye, I.},
        keywords = {Behavioral research; Computer vision; Convolutional neural networks; Decoding; Signal encoding, Chest X-ray image; Course information; Medical professionals; Radiology reports; Sequential dependencies; Short term memory; State-of-the-art methods; Time-consuming tasks, Medical imaging},
            issn = {18650929},
            isbn = {9789811540141},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083724536&doi=10.1007\%2f978-981-15-4015-8\%5f15&partnerID=40&md5=e28028f91eaaf6e681e9fa1574c112b0}
}