Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Authors = Kim-Alessandro Weber

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 2573 KiB  
Article
Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort
by Martina Greselin, Po-Jui Lu, Lester Melie-Garcia, Mario Ocampo-Pineda, Riccardo Galbusera, Alessandro Cagol, Matthias Weigel, Nina de Oliveira Siebenborn, Esther Ruberte, Pascal Benkert, Stefanie Müller, Sebastian Finkener, Jochen Vehoff, Giulio Disanto, Oliver Findling, Andrew Chan, Anke Salmen, Caroline Pot, Claire Bridel, Chiara Zecca, Tobias Derfuss, Johanna M. Lieb, Michael Diepers, Franca Wagner, Maria I. Vargas, Renaud Du Pasquier, Patrice H. Lalive, Emanuele Pravatà, Johannes Weber, Claudio Gobbi, David Leppert, Olaf Chan-Hi Kim, Philippe C. Cattin, Robert Hoepner, Patrick Roth, Ludwig Kappos, Jens Kuhle and Cristina Granzieraadd Show full author list remove Hide full author list
Bioengineering 2024, 11(8), 858; https://doi.org/10.3390/bioengineering11080858 - 22 Aug 2024
Cited by 2 | Viewed by 1947
Abstract
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for [...] Read more.
The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support. Full article
Show Figures

Graphical abstract

28 pages, 6231 KiB  
Article
Key Experiment and Quantum Reasoning
by Moritz Waitzmann, Kim-Alessandro Weber, Susanne Wessnigk and Ruediger Scholz
Physics 2022, 4(4), 1202-1229; https://doi.org/10.3390/physics4040078 - 8 Oct 2022
Cited by 5 | Viewed by 3118
Abstract
For around five decades, physicists have been experimenting with single quanta such as single photons. Insofar as the practised ensemble reasoning has become obsolete for the interpretation of these experiments, the non-classical intrinsic probabilistic nature of quantum theory has gained increased importance. One [...] Read more.
For around five decades, physicists have been experimenting with single quanta such as single photons. Insofar as the practised ensemble reasoning has become obsolete for the interpretation of these experiments, the non-classical intrinsic probabilistic nature of quantum theory has gained increased importance. One of the most important exclusive features of quantum physics is the undeniable existence of the superposition of states, even for single quantum objects. One known example of this effect is entanglement. In this paper, two classically contradictory phenomena are combined to one single experiment. This experiment incontestably shows that a single photon incident on an optical beam splitter can either be reflected or transmitted. The almost complete absence of coincident clicks of two photodetectors demonstrates that these two output states are incompatible. However, when combining these states using two mirrors, we can observe interference patterns in the counting rate of the single photon detector. The only explanation for this is that the two incompatible output states are prepared and kept simultaneously—a typical consequence of a quantum superposition of states. (Semi-)classical physical concepts fail here, and a full quantum concept is predestined to explain the complementary experimental outcomes for the quantum optical “non-waves” called single photons. In this paper, we intend to demonstrate that a true quantum physical key experiment (“true” in the sense that it cannot be explained by any classical physical concept), when combined with full quantum reasoning (probability, superposition and interference), influences students’ readiness to use quantum elements for interpretation. Full article
(This article belongs to the Special Issue Teaching and Learning Quantum Theory and Particle Physics)
Show Figures

Figure 1

Back to TopTop