Search Results for author: Magdalini Paschali

Found 21 papers, 5 papers with code

Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET

no code implementations17 May 2023 Francesca De Benetti, Walter Simson, Magdalini Paschali, Hasan Sari, Axel Romiger, Kuangyu Shi, Nassir Navab, Thomas Wendler

Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.

Self-Supervised Learning

Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing

1 code implementation28 Jul 2022 Magdalini Paschali, Qingyu Zhao, Ehsan Adeli, Kilian M. Pohl

A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i. e., whether certain factors (e. g., related to life events) are associated with an outcome (e. g., depression).

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

no code implementations17 Mar 2022 Tobias Czempiel, Aidean Sharghi, Magdalini Paschali, Nassir Navab, Omid Mohareri

Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

no code implementations29 Jul 2021 Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler

Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.

Clustering Decision Making +1

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

1 code implementation12 Mar 2021 Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.

Computed Tomography (CT) COVID-19 Image Segmentation +2

OperA: Attention-Regularized Transformers for Surgical Phase Recognition

no code implementations5 Mar 2021 Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab

In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.

Surgical phase recognition

Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

3 code implementations30 Mar 2020 Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli, Magdalini Paschali, Walter Simson, Emad Fatemizadeh, Nassir Navab

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.

reinforcement-learning Reinforcement Learning (RL)

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

2 code implementations24 Mar 2020 Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems.

Surgical phase recognition

Radar Emitter Classification with Attribute-specific Recurrent Neural Networks

no code implementations18 Nov 2019 Paolo Notaro, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab

Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classification.

Classification General Classification

Signal Clustering with Class-independent Segmentation

no code implementations18 Nov 2019 Stefano Gasperini, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab

Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.

Clustering Image Segmentation +1

End-to-End Learning-Based Ultrasound Reconstruction

no code implementations9 Apr 2019 Walter Simson, Rüdiger Göbl, Magdalini Paschali, Markus Krönke, Klemens Scheidhauer, Wolfgang Weber, Nassir Navab

The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.

Image Reconstruction

Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness

no code implementations14 Jan 2019 Magdalini Paschali, Walter Simson, Abhijit Guha Roy, Muhammad Ferjad Naeem, Rüdiger Göbl, Christian Wachinger, Nassir Navab

Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.

Data Augmentation General Classification +2

Generalizability vs. Robustness: Adversarial Examples for Medical Imaging

no code implementations23 Mar 2018 Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, Nassir Navab

In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data.

Brain Segmentation General Classification +2

Learning Robust Hash Codes for Multiple Instance Image Retrieval

no code implementations16 Mar 2017 Sailesh Conjeti, Magdalini Paschali, Amin Katouzian, Nassir Navab

In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval.

Image Retrieval Pose Retrieval +1

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