Persona: García Seco de Herrera, Alba
Cargando...
Dirección de correo electrónico
ORCID
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
García Seco de Herrera
Nombre de pila
Alba
Nombre
13 resultados
Resultados de la búsqueda
Mostrando 1 - 10 de 13
Publicación Medical images modality classification using discrete Bayesian networks(Elsevier, 2016-10) Arias, Jacinto; Martínez-Gómez, Jesús; Gámez, Jose A.; García Seco de Herrera, Alba; Müller, HenningIn this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes.Publicación Effect of data leakage in brain MRI classification using 2D convolutional neural networks(Nature Research, 2021-11-19) Yagis, Ekin; Workalemahu Atnafu, Selamawet; García Seco de Herrera, Alba; Marzi, Chiara; Scheda, Riccardo; Giannelli, Marco; Tessa, Carlo; Citi, Luca; Diciotti, StefanoIn recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson’s Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.Publicación An OCR Post-Correction Approach Using Deep Learning for Processing Medical Reports(Institute of Electrical and Electronics Engineers, 2021-06-08) Karthikeyan, Srinidhi; García Seco de Herrera, Alba; Doctor, Faiyaz; Mirza, Asim; https://orcid.org/0000-0001-6863-0760; https://orcid.org/0000-0002-6509-5325; https://orcid.org/0000-0002-8412-5489According to a recent Deloitte study, the COVID-19 pandemic continues to place a huge strain on the global health care sector. Covid-19 has also catalysed digital transformation across the sector for improving operational efficiencies. As a result, the amount of digitally stored patient data such as discharge letters, scan images, test results or free text entries by doctors has grown significantly. In 2020, 2314 exabytes of medical data was generated globally. This medical data does not conform to a generic structure and is mostly in the form of unstructured digitally generated or scanned paper documents stored as part of a patient’s medical reports. This unstructured data is digitised using Optical Character Recognition (OCR) process. A key challenge here is that the accuracy of the OCR process varies due to the inability of current OCR engines to correctly transcribe scanned or handwritten documents in which text may be skewed, obscured or illegible. This is compounded by the fact that processed text is comprised of specific medical terminologies that do not necessarily form part of general language lexicons. The proposed work uses a deep neural network based self-supervised pre-training technique: Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) that can learn to predict hidden (masked) sections of text to fill in the gaps of non-transcribable parts of the documents being processed. Evaluating the proposed method on domain-specific datasets which include real medical documents, shows a significantly reduced word error rate demonstrating the effectiveness of the approach.Publicación Foot Recognition Using Deep Learning for Knee Rehabilitation(ASET, 2019) Duangsoithong, Rakkrit; Jaruenpunyasak, Jermphiphut; García Seco de Herrera, AlbaThe use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.Publicación Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition(IEEE_ Institute of Electrical and Electronics Engineers, 2020-10-27) Sae Jong, Nida; García Seco de Herrera, AlbaSpeech disorders such as dysarthria are common and frequent after suffering a stroke. Speech rehabilitation performed by a speech-language pathologist is needed to improve and recover. However, in Thailand, there is a shortage of speech-language pathologists. In this paper, we present a syllable recognition system, which can be deployable in a speech rehabilitation system to provide support to the limited speech-language pathologists available. The proposed system is based on a multimodal fusion of acoustic signal and surface electromyography (sEMG) collected from facial muscles. Multimodal data fusion is studied to improve signal collection under noisy situations while reducing the number of electrodes needed. The signals are simultaneously collected while articulating 12 Thai syllables designed for rehabilitation exercises. Several features are extracted from sEMG signals and five channels are studied. The best combination of features and channels is chosen to be fused with the mel-frequency cepstral coefficients extracted from the acoustic signal. The feature vector from each signal source is projected by spectral regression extreme learning machine and concatenated. Data from seven healthy subjects were collected for evaluation purposes. Results show that the multimodal fusion outperforms the use of a single signal source achieving up to ~ 98% of accuracy. In other words, an accuracy improvement up to 5% can be achieved when using the proposed multimodal fusion. Moreover, its low standard deviations in classification accuracy compared to those from the unimodal fusion indicate the improvement in the robustness of the syllable recognition.Publicación Shangri–La: A medical case–based retrieval tool(Wiley, 2018-11-28) García Seco de Herrera, Alba; Schaer, Roger; Müller, HenningLarge amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Information retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri–La is a medical retrieval system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying a case description and attached images. The system is based on a multimodal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query–adaptive multimodal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multimodal approach submitted to ImageCLEFmed 2013.Publicación Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods(MDPI, 2022-03-04) Jaruenpunyasak, Jermphiphut; García Seco de Herrera, Alba; Duangsoithong, RakkritLower-body detection can be useful in many applications, such as the detection of falling and injuries during exercises. However, it can be challenging to detect the lower-body, especially under various lighting and occlusion conditions. This paper presents a novel lower-body detection framework using proposed anthropometric ratios and compares the performance of deep learning (convolutional neural networks and OpenPose) and traditional detection methods. According to the results, the proposed framework helps to successfully detect the accurate boundaries of the lower-body under various illumination and occlusion conditions for lower-limb monitoring. The proposed framework of anthropometric ratios combined with convolutional neural networks (A-CNNs) also achieves high accuracy (90.14%), while the combination of anthropometric ratios and traditional techniques (A-Traditional) for lower-body detection shows satisfactory performance with an averaged accuracy (74.81%). Although the accuracy of OpenPose (95.82%) is higher than the A-CNNs for lower-body detection, the A-CNNs provides lower complexity than the OpenPose, which is advantageous for lower-body detection and implementation on monitoring systems.Publicación Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection(Nature Research, 2024-09-06) Islam, Shafiqul; Wishart, Gordon C.; Walls, Joseph; Hall, Per; García Seco de Herrera, Alba; Gan, John Q.; Raza, HaiderMelanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for patients with a possible melanoma. However, the 7PCL method only considers seven meta-features to calculate a risk score and is only relevant for patients with suspected melanoma. There are limited studies on the extensive use of patient metadata for the detection of all skin cancer subtypes. This study investigates artificial intelligence (AI) models that utilise patient metadata consisting of 23 attributes for suspicious skin lesion detection. We have identified a new set of most important risk factors, namely “C4C risk factors”, which is not just for melanoma, but for all types of skin cancer. The performance of the C4C risk factors for suspicious skin lesion detection is compared to that of the 7PCL and the Williams risk factors that predict the lifetime risk of melanoma. Our proposed AI framework ensembles five machine learning models and identifies seven new skin cancer risk factors: lesion pink, lesion size, lesion colour, lesion inflamed, lesion shape, lesion age, and natural hair colour, which achieved a sensitivity of 80.46 ± 2.50% and a specificity of 62.09 ± 1.90% in detecting suspicious skin lesions when evaluated using the metadata of 53,601 skin lesions collected from different skin cancer diagnostic clinics across the UK, significantly outperforming the 7PCL-based method (sensitivity 68.09 ± 2.10%, specificity 61.07 ± 0.90%) and the Williams risk factors (sensitivity 66.32 ± 1.90%, specificity 61.71 ± 0.6%). Furthermore, through weighting the seven new risk factors we came up with a new risk score, namely “C4C risk score”, which alone achieved a sensitivity of 76.09 ± 1.20% and a specificity of 61.71 ± 0.50%, significantly outperforming the 7PCL-based risk score (sensitivity 73.91 ± 1.10%, specificity 49.49 ± 0.50%) and the Williams risk score (sensitivity 60.68 ± 1.30%, specificity 60.87 ± 0.80%). Finally, fusing the C4C risk factors with the 7PCL and Williams risk factors achieved the best performance, with a sensitivity of 85.24 ± 2.20% and a specificity of 61.12 ± 0.90%. We believe that fusing these newly found risk factors and new risk score with image data will further boost the AI model performance for suspicious skin lesion detection. Hence, the new set of skin cancer risk factors has the potential to be used to modify current skin cancer referral guidelines for all skin cancer subtypes, including melanoma.Publicación ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset(Nature Research, 2024-06-24) Rückert, Johannes; Bloch, Louise; Brünge, Raphael; Idrissi-Yaghir, Ahmad; Schäfer, Henning; Schmidt, Cynthia S.; Koitka, Sven; Pelka, Obioma; Ben Abacha, Asma; García Seco de Herrera, Alba; Müller, Henning; Horn, Peter A.; Nensa, Felix; Friedrich, Christoph M.; https://orcid.org/0000-0002-5038-5899; https://orcid.org/0000-0001-7540-4980; https://orcid.org/0000-0002-6046-4048; https://orcid.org/0000-0003-1507-9690; https://orcid.org/0000-0002-4123-0406; https://orcid.org/0000-0003-1994-0687; https://orcid.org/0000-0001-9704-1180; https://orcid.org/0000-0001-5156-4429Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.Publicación A review on segmentation of knee articular cartilage: from conventional methods towards deep learning(Elsevier, 2020-06) Ebrahimkhani, Somayeh; Jaward, Mohamed Hisham; Cicuttini, Flavia M.; Dharmaratne, Anuja; Wang, Yuanyuan; García Seco de Herrera, AlbaIn this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).