The discussion about deep disagreement has gained significant momentum in the last several years. This discussion often relies on the intuition that deep disagreement is, in some sense, rationally irresolvable. In this paper, I will provide a theory of rationally irresolvable disagreement. Such a theory is interesting in its own right, since it conflicts with the view that rational attitudes and procedures are paradigmatic tools for resolving disagreement. Moreover, I will suggest replacing discussions about deep disagreement with an analysis of rationally irresolvable disagreement, since this notion can be more clearly defined than deep disagreement and captures the basic intuitions underlying deep disagreement. I will first motivate this project by critically assessing the current debate about deep disagreement. I then detail the notions of rationality and resolvable disagreement which are crucial for a suitable theory of rationally irresolvable disagreement before sketching various instances of rationally irresolvable disagreement. Finally, I argue for replacing theories of deep disagreement with theories of rationally irresolvable disagreement, an approach that has significant advantages over existing theories of deep disagreement which focus on hinge propositions or fundamental epistemic principles.
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http://dx.doi.org/10.1007/s11098-023-01933-7 | DOI Listing |
NMR Biomed
January 2025
Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.
Fluorine-19 (F) MRI has become an established tool for in vivo cell tracking following ex vivo or in vivo labelling of various cell types with F perfluorocarbons (PFCs). Here, we developed and evaluated novel mouse-specific radiofrequency (RF) hardware for improved dual H anatomical imaging and deep tissue F MR detection of PFCs. Three linearly polarized birdcage RF coils were constructed-a dual-frequency H/F coil, and a pair of single-frequency H and F coils, designed to be used sequentially.
View Article and Find Full Text PDFRadiol Artif Intell
November 2024
From the Department of Radiology, Radiology Informatics Laboratory, Mayo Clinic, 200 1st St SW, Rochester, MN 55905.
Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in detection as well as model accuracy in identifying challenging cases. Materials and Methods This was a retrospective (November 2017 through December 2017) study of 491 noncontrast head CT volumes from the CQ500 dataset in which three senior radiologists annotated sections containing ICH. The dataset was split into definite and challenging (uncertain) subsets, where challenging images were defined as those in which there was disagreement among readers.
View Article and Find Full Text PDFEur J Surg Oncol
November 2024
UOC Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Background: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outcomes. However, the lack of external validation of predictive models, missing in several published articles, is a major issue that can potentially limit the reliability and applicability of predictive models in clinical settings. Therefore, this systematic review described different externally validated methods of predicting response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients and how they could improve clinical decision-making.
View Article and Find Full Text PDFEpilepsia
November 2024
Department of Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands.
Objective: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of experts to assess its potential applicability.
Methods: First, we performed clinical validation on an internal data set.
J Am Med Inform Assoc
January 2025
Laboratory of Experimental Cardiology, University of Copenhagen, 2200 Copenhagen, Denmark.
Objective: Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods.
Materials And Methods: A residual deep neural network was trained on ECGs to predict intervals and amplitudes. Nine commonly used explanation methods (Saliency, Deconvolution, Guided backpropagation, Gradient SHAP, SmoothGrad, Input × gradient, DeepLIFT, Integrated gradients, GradCAM) were qualitatively evaluated by medical experts and objectively evaluated using a perturbation-based method.
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