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Browsing C-BER - Indexed Articles in Journals by Author "8251"
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ItemLightweight multi-scale classification of chest radiographs via size-specific batch normalization( 2023) Sofia Cardoso Pereira ; Rocha,J ; Campilho,A ; Sousa,P ; Mendonça,AM ; 8251Background and Objective:Convolutional neural networks are widely used to detect radiological findings in chest radiographs. Standard architectures are optimized for images of relatively small size (for example, 224 × 224 pixels), which suffices for most application domains. However, in medical imaging, larger inputs are often necessary to analyze disease patterns. A single scan can display multiple types of radiological findings varying greatly in size, and most models do not explicitly account for this. For a given network, whose layers have fixed-size receptive fields, smaller input images result in coarser features, which better characterize larger objects in an image. In contrast, larger inputs result in finer grained features, beneficial for the analysis of smaller objects. By compromising to a single resolution, existing frameworks fail to acknowledge that the ideal input size will not necessarily be the same for classifying every pathology of a scan. The goal of our work is to address this shortcoming by proposing a lightweight framework for multi-scale classification of chest radiographs, where finer and coarser features are combined in a parameter-efficient fashion. Methods:We experiment on CheXpert, a large chest X-ray database. A lightweight multi-resolution (224 × 224, 448 × 448 and 896 × 896 pixels) network is developed based on a Densenet-121 model where batch normalization layers are replaced with the proposed size-specific batch normalization. Each input size undergoes batch normalization with dedicated scale and shift parameters, while the remaining parameters are shared across sizes. Additional external validation of the proposed approach is performed on the VinDr-CXR data set. Results:The proposed approach (AUC 83.27±0.17, 7.1M parameters) outperforms standard single-scale models (AUC 81.76±0.18, 82.62±0.11 and 82.39±0.13 for input sizes 224 × 224, 448 × 448 and 896 × 896, respectively, 6.9M parameters). It also achieves a performance similar to an ensemble of one individual model per scale (AUC 83.27±0.11, 20.9M parameters), while relying on significantly fewer parameters. The model leverages features of different granularities, resulting in a more accurate classification of all findings, regardless of their size, highlighting the advantages of this approach. Conclusions:Different chest X-ray findings are better classified at different scales. Our study shows that multi-scale features can be obtained with nearly no additional parameters, boosting performance. © 2023 The Author(s)
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ItemMapping Cashew Orchards in Cantanhez National Park (Guinea-Bissau)( 2022) Sofia Cardoso Pereira ; Lopes,C ; João Pedro Pedroso ; 8251 ; 4747The forests and woodlands of Guinea-Bissau are a biodiversity hotspot under threat, which are progressively being replaced by cashew tree orchards. While the exports of cashew nuts significantly contribute to the gross domestic product and support local livelihoods, the country's natural capital is under significant pressure due to unsustainable land use. In this context, official entities strive to counter deforestation, but the problem persists, and there are currently no systematic or automated means for objectively monitoring and reporting the situation. Furthermore, previous remote sensing approaches failed to distinguish cashew orchards from forests and woodlands due to the significant spectral overlap between the land cover types and the highly intertwined structure of the cashew tree patches. This work contributes to overcoming such difficulty. It develops an affordable, reliable, and easy-to-use procedure based on machine learning models and Sentinel-2 images, automatically detecting cashew orchards with a dice coefficient of 82.54%. The results of this case study designed for the Cantanhez National Park are proof of concept and demonstrate the viability of mapping cashew orchards. Therefore, the work is a stepping stone towards wall-to-wall operational monitoring in the region. © 2022 Elsevier B.V.