Search Results for author: Matthias Keicher

Found 16 papers, 8 papers with code

RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance

1 code implementation30 Nov 2023 Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab, Matthias Keicher

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.

Language Modelling Large Language Model

Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting

1 code implementation11 Jul 2023 Chantal Pellegrini, Matthias Keicher, Ege Özsoy, Nassir Navab

However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods.

Medical Visual Question Answering Question Answering +2

Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays

1 code implementation24 Mar 2023 Yiheng Xiong, Jingsong Liu, Kamilia Zaripova, Sahand Sharifzadeh, Matthias Keicher, Nassir Navab

The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods.

Decision Making Multi-Label Classification +1

U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images

no code implementations16 Jun 2022 Marcel Kollovieh, Matthias Keicher, Stephan Wunderlich, Hendrik Burwinkel, Thomas Wendler, Nassir Navab

To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD.

FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays

no code implementations29 Mar 2022 Matthias Keicher, Kamilia Zaripova, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab

The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task.

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

GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images

no code implementations19 Mar 2021 Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim, Nassir Navab

Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.


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

Continual Class Incremental Learning for CT Thoracic Segmentation

no code implementations12 Aug 2020 Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher, Josep Henry, Paul Thomson, Nassir Navab

In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset.

Class Incremental Learning Incremental Learning +2

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

no code implementations2 May 2020 Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi

Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.

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

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