Research Presentation Sessions: Imaging Informatics / Artificial Intelligence and Machine Learning
RPS 1305 - Artificial intelligence (AI) in chest imaging: part 2
Lectures
1
Introduction by the moderator
02:00Guillaume Chassagnon, Paris / FR
2
Overall survival prediction for stage II and stage III non-small cell lung cancer patients using a graph-based deep learning algorithm
08:00Varut Vardhanabhuti, Hong Kong / HK
3
A laboratory-medicine-like approach to the analysis of unremarkable chest radiographs using artificial intelligence
08:00Thomas Weikert, Basel / CH
4
Deep learning universal lesion segmentation for automated RECIST measurements on CT: comparison to manual assessment by radiologists
08:00Max De Grauw, Velp (GE) / NL
5
Lightweight techniques to improve generalisability of U-Net based segmentations of lung lobes
08:00Armin Dadras, Offenbach / DE
6
Development and validation of a machine learning based CADx designed to improve patient management in lung cancer screening programmes
08:00Charles Voyton, Valbonne / FR
7
First performance evaluation of an artificial intelligence-based computer aided detection system for pulmonary nodule evaluation in dual source photon-counting detector CT at different low dose levels
08:00Lisa Jungblut, Zürich / CH
8
Reducing clinical chest X-ray reading times by using artificial intelligence to stratify worklists into normal/abnormal categories
08:00K.F.M. Hergaarden, Leiden / NL
9
Development of a deep learning-based model for chest X-ray quality assessment
08:00Rémi Khansa, Taissy / FR
10
AI model uncertainty for detecting pneumothorax on chest radiographs is a strong predictor for annotator confidence
08:00Omar Hertgers, Den Haag / NL
11
Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease
08:00Sebastian Röhrich, Vienna / AT
12
CAD significantly increases the accuracy of pulmonary nodule detection in both concurrent and second reader paradigms
08:00Yiyuan Zhao, Chesterbrook / US