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309 results on '"Radeva, Petia"'

25. Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images.

Author

Chatterjee, Soumick, Saad, Fatima, Sarasaen, Chompunuch, Ghosh, Suhita, Krug, Valerie, Khatun, Rupali, Mishra, Rahul, Desai, Nirja, Radeva, Petia, Rose, Georg, Stober, Sebastian, Speck, Oliver, and Nürnberger, Andreas

Subjects
X-ray imaging, X-rays, MEDICAL personnel, COVID-19, COVID-19 pandemic, COMPUTER-assisted image analysis (Medicine)
Abstract

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods—occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT—and using a global technique—neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

Author

Balocco, Simone, Gatta, Carlo, Ciompi, Francesco, Wahle, Andreas, Radeva, Petia, Carlier, Stephane, Unal, Gozde, Sanidas, Elias, Mauri, Josepa, Carillo, Xavier, Kovarnik, Tomas, Wang, Ching-Wei, Chen, Hsiang-Chou, Exarchos, Themis P., Fotiadis, Dimitrios I., Destrempes, François, Cloutier, Guy, Pujol, Oriol, Alberti, Marina, Mendizabal-Ruiz, E. Gerardo, Rivera, Mariano, Aksoy, Timur, Downe, Richard W., and Kakadiaris, Ioannis A.

Published
2014
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46. Hercules : Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification.

Author

Abdar, Moloud, Fahami, Mohammad Amin, Rundo, Leonardo, Radeva, Petia, Frangi, Alejandro F., Acharya, U. Rajendra, Khosravi, Abbas, Lam, Hak-Keung, Jung, Alexander, and Nahavandi, Saeid

Abstract

The automatic and accurate analysis of medical images (e.g., segmentation,detection, classification) are prerequisites for modern disease diagnosis and prognosis. Computer-aided diagnosis (CAD) systems empower accurate and effective detection of various diseases and timely treatment decisions. The past decade witnessed a spur in deep learning (DL)-based CADs showing outstanding performance across many health care applications. Medical imaging is hindered by multiple sources of uncertainty ranging fromnteasurement (aleatoric) errors, physiological variability, and limited medical knowledge (epistemic errors). However, uncertainty quantification (UQ) in most existing DL methods is insufficiently investigated, particularly in medical image analysis. Therefore, to address this gap, in this article, we propose a simple yet novel hierarchical attentive multilevel feature fusion model with an uncertainty-aware module for medical image classification coined Hercules. This approach is tested on several real medical image classification challenges. The proposed Hercules model consists of two main feature fusion blocks, where the former concentrates on attention-based fusion with uncertainty quantification module and the latter uses the raw features. Hercules was evaluated across three medical imaging datasets, i.e., retinal OCT, lung CT, and chest X-ray. Hercules produced the best classification accuracy in retinal OCT (94.21%), lung CT (99.59%), and chest X-ray (96.50%) datasets, respectively, against other state-of-the-art medical image classification methods. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Telomere length is causally connected to brain MRI image derived phenotypes: A mendelian randomization study.

Author

Salih, Ahmed, Galazzo, Ilaria Boscolo, Petersen, Steffen E., Lekadir, Karim, Radeva, Petia, Menegaz, Gloria, and Altmann, André

Subjects
MAGNETIC resonance imaging, TELOMERES, WHITE matter (Nerve tissue), BRAIN imaging, PHENOTYPES, DEGENERATION (Pathology)
Abstract

Recent evidence suggests that shorter telomere length (TL) is associated with neuro degenerative diseases and aging related outcomes. The causal association between TL and brain characteristics represented by image derived phenotypes (IDPs) from different magnetic resonance imaging (MRI) modalities remains unclear. Here, we use two-sample Mendelian randomization (MR) to systematically assess the causal relationships between TL and 3,935 brain IDPs. Overall, the MR results suggested that TL was causally associated with 193 IDPs with majority representing diffusion metrics in white matter tracts. 68 IDPs were negatively associated with TL indicating that longer TL causes decreasing in these IDPs, while the other 125 were associated positively (longer TL leads to increased IDPs measures). Among them, ten IDPs have been previously reported as informative biomarkers to estimate brain age. However, the effect direction between TL and IDPs did not reflect the observed direction between aging and IDPs: longer TL was associated with decreases in fractional anisotropy and increases in axial, radial and mean diffusivity. For instance, TL was positively associated with radial diffusivity in the left perihippocampal cingulum tract and with mean diffusivity in right perihippocampal cingulum tract. Our results revealed a causal role of TL on white matter integrity which makes it a valuable factor to be considered when brain age is estimated and investigated. [ABSTRACT FROM AUTHOR]

Published
2022
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