This article reports the lessons learnt from a period of retraining and from discussion with others who have been involved in a similar process. The conclusions are that retraining should only be undertaken once there is full agreement between all parties involved that it is necessary and feasible. There must also be agreement in advance of the criteria which will constitute successful retraining, and the actions which will be taken to ensure the rapid return of the retrainee to the type of practice which is being offered and has been accepted. The process of retraining requires especially close supervision and is very stressful for the retrainee. It is likely that this should only be undertaken in units specially staffed and funded to accommodate this type of work.
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http://dx.doi.org/10.1016/s1479-666x(05)80088-5 | DOI Listing |
Insights Imaging
January 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Purposes: The presence of clinically significant prostate cancer (csPCa) is equivocal for patients with prostate imaging reporting and data system (PI-RADS) category 3. We aim to develop deep learning models for re-stratify risks in PI-RADS category 3 patients.
Methods: This retrospective study included a bi-parametric MRI of 1567 consecutive male patients from six centers (Centers 1-6) between Jan 2015 and Dec 2020.
J Chem Inf Model
January 2025
Department of Chemistry, Kansas State University, Manhattan, Kansas 66506, United States.
Gold nanoparticles can exhibit unique physical and chemical properties, such as plasmon resonances or photoluminescence. These nanoparticles have many atoms, which leads to high computational costs for density functional theory (DFT) calculations. In this work, we used the FLARE++ (fast learning of atomistic rare events) code and incorporated an active learning algorithm to construct force fields for gold thiolate-protected nanoclusters.
View Article and Find Full Text PDFIn this manuscript, a principle-driven fiber transmission model for short-distance transmission with parameterized inputs is put forward. By taking into account the previously proposed principle-driven fiber model as the basic solution solver, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrödinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model. Once adopted, this model can have prominent advantages in both computation efficiency and physical background.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Cellular Stress Biology, Institute of Artificial Intelligence, School of Life Sciences, Faculty of Medicine and Life Sciences, National Institute for Data Science in Health and Medicine, XMU-HBN skin biomedical research center, Xiamen University, Xiamen, Fujian 361102, China.
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Artifcial Intelligence, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea.
Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.
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