Results 31 to 40 of about 424,704 (329)

Towards Explainable Pedestrian Behavior Prediction: A Neuro-Symbolic Framework for Autonomous Driving

Applied Sciences
In the context of autonomous driving, predicting pedestrian behavior is a critical component for enhancing road safety. Currently, the focus of such predictions extends beyond accuracy and reliability, placing increasing emphasis on the explainability ...
Angie Nataly Melo Castillo, Carlota Salinas Maldonado, Miguel Ángel Sotelo   +2 more
doaj   +1 more source

Roentgen Interpretation

Southern Medical Journal, 1921
Includes bibliographies. ; Mode of access: Internet.
Holmes, George Winslow, 1876-, Ruggles, Howard Edwin, 1886-1939.   +1 more
openaire   +1 more source

Interpreting Deep Visual Representations via Network Dissection

, 2018
The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data.
Bau, David, Oliva, Aude, Torralba, Antonio, Zhou, Bolei   +3 more
core   +1 more source

Dexamethasone for Chemotherapy‐Induced Nausea and Vomiting Prevention in Pediatric Patients: International Consensus

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background An international Delphi panel of experts developed consensus statements to delineate the circumstances where the risks of dexamethasone as an antiemetic do and do not outweigh its benefits. Procedure Experts in supportive care of pediatric patients were invited to participate.
Negar Shavandi, Paula D. Robinson, Shana Jacobs, L. Lee Dupuis, the International Pediatric Dexamethasone Panel, Sameer Bakhshi, Tiene Bauters, Eva Brack, Simone Cesaro, Claire Fosbrook, Jason Freedman, Tamara MacDonald, Eloise Neumann, Venkatraman Radhakrishnan, Rod Rassekh, Florian Slimano, Johnathan Soggee, Joel Thompson, Marianne van de Wetering, Milena Villarroel, Jasper Wong   +20 more
wiley   +1 more source

Kolmogorov–Arnold Networks for System Identification of First- and Second-Order Dynamic Systems

Engineering Proceedings
System identification—originating in the 1950s from statistical theory—has since developed a wealth of algorithms, insights, and practical expertise.
Lily Chiparova, Vasil Popov
doaj   +1 more source

Interpreting (un)interpretability [PDF]

Theoretical Linguistics, 2019
published
Walkden, George, Breitbarth, Anne
openaire   +2 more sources

Real‐World Pediatric Blinatumomab Administration: Access to Outpatient Care Delivery and Impact of a Hospital‐Dispensed Model

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Blinatumomab has been shown to be highly effective for patients with pediatric B‐ALL and has recently become standard of care therapy. Due to its past use in the clinical trial setting, there is limited information available about real‐world administration.
Katelyn Oranges, Kelly A. Duffy, Sara McDonough, Evelyn Kurpiewski, R. Jennifer Randall, Allison Barz Leahy, Haley M. Newman, Joyce P. Lee, Stephen P. Hunger, Anne F. Reilly, David T. Teachey, Susan R. Rheingold, Daniel J. Zheng   +12 more
wiley   +1 more source

Eye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration

Bioengineering
Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification.
Jiangxia Duan, Meiwei Zhang, Minghui Song, Xiaopan Xu, Hongbing Lu   +4 more
doaj   +1 more source

Clinical Insights Into Hypercalcemia of Malignancy in Childhood

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Hypercalcemia of malignancy (HCM) is a rare but life‐threatening metabolic emergency in children that occurs in less than 1% of pediatric cancer cases, with a reported incidence ranging from 0.4% to 1.0% across different studies. While it is observed in 10%–20% of adult malignancies, pediatric HCM remains relatively uncommon.
Hüseyin Anıl Korkmaz
wiley   +1 more source

Investigating the Performance of the Attention Mechanism and the Interpretability in the Concrete Strength Prediction Model

Buildings
To address the limitations of traditional models in capturing complex features for concrete strength prediction, this study proposes a hybrid deep learning approach that integrates multiple attention mechanisms with gated recurrent units (GRU).
Ziang Jia, Noor Azline Mohd Nasir, Nabilah Abu Bakar   +2 more
doaj   +1 more source