P
Petros-Pavlos Ypsilantis
Researcher at King's College London
Publications - 5
Citations - 370
Petros-Pavlos Ypsilantis is an academic researcher from King's College London. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 5, co-authored 5 publications receiving 290 citations.
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Journal ArticleDOI
Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.
Petros-Pavlos Ypsilantis,Musib Siddique,Hyon Mok Sohn,Andrew Davies,Gary Cook,Vicky Goh,Giovanni Montana +6 more
TL;DR: Experimental results provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy, and compare the performance of two competing radiomics strategies.
Journal ArticleDOI
Learning to detect chest radiographs containing pulmonary lesions using visual attention networks
Emanuele Pesce,Samuel Joseph Withey,Petros-Pavlos Ypsilantis,Robert Bakewell,Vicky Goh,Giovanni Montana +5 more
TL;DR: Two novel neural network architectures to detect pulmonary lesions in chest x‐rays imagesthat use visual attention mechanisms are proposed, designed to learn from a large number of weakly‐labelled images and a small number of annotated images.
Journal ArticleDOI
Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting
Lakshmana Ayaru,Petros-Pavlos Ypsilantis,Abigail Nanapragasam,Ryan Chang-Ho Choi,Anish Thillanathan,Lee Min-Ho,Giovanni Montana +6 more
TL;DR: The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models.
Posted Content
Learning what to look in chest X-rays with a recurrent visual attention model
TL;DR: A stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality is presented.
Posted Content
Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging.
TL;DR: It is demonstrated that leveraging intra-slice dependencies substantially increases the sensitivity to detect pulmonary nodules without inflating the false positive rate and Comparisons with a competing multi-channel convolutional neural network for multi-slice segmentation and other published methodologies using the same dataset provide evidence that ReCTnet offers significant performance gains.