Results 31 to 40 of about 244,685 (311)

Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform

IEEE Access
Incorporating deep learning into computer-aided medical diagnosis has led to significant advancements. However, a major challenge remains in interpreting deep learning models, especially in identifying the features critical for diagnosis.
Daigo Takano, Hajime Omura, Teruya Minamoto   +2 more
doaj   +1 more source

Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

Axioms, 2023
In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing ...
Zhouyuan Chen, Zhichao Lian, Zhe Xu
doaj   +1 more source

Evidence‐Informed Multidisciplinary Consensus Guidance for the Psychosocial Care of Adolescents With High‐Risk Cancer: Recommendations From the Italian Association of Pediatric Hematology and Oncology

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Adolescents with high‐risk cancer face complex developmental, psychosocial, and ethical challenges that extend beyond disease‐directed treatment. Although international recommendations exist for communication, psychosocial care, pediatric palliative care, survivorship, and shared decision‐making, these have largely evolved within ...
Johanna M. C. Blom, Dorella Scarponi, Giulia Zucchetti, Teresa Perillo, Maria Montanaro, Elisa Marconi, Chiara Rutigliano, Paolo Colavero, Paola Quarello, Marina Bertolotti, Monica Cellini, Carlo Clerici, Maurizio Mascarin, Francesca Bomben, Andrea Ferrari, the expert workgroup   +15 more
wiley   +1 more source

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

Early Body Mass Index z‐Score Change and Resolution of Severe Malnutrition in Children With Sickle Cell Anemia in a Low‐Income Setting: A Prospective Single‐Arm Extension Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Children with sickle cell anemia (SCA) in low‐income settings are at risk of severe malnutrition, but optimal nutritional management has not been established. We evaluated an intensified ready‐to‐use therapeutic food (RUTF) regimen in children with persistent severe malnutrition after initial treatment and assessed whether early ...
Safiya Gambo, Shehu Umar Abdullahi, Hassan Adam Murtala, Halima Kabir, Khadija A. Shamsu, Sari A. Acra, Virginia A. Stallings, Mark Rodeghier, Michael R. DeBaun, Lauren J. Klein   +9 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

Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation. [PDF]

, 2021
In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup).
Shao, Ling, Poellinger, Alexander, Reyes, Mauricio, Mahapatra, Dwarikanath   +3 more
core   +1 more source

Central Nervous System Tumors Among Infants in Canada: A Report From CYP‐C

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Central nervous system (CNS) tumors in infants are rare, pose unique clinical challenges, and lack large‐scale evidence‐based data to guide management. This study seeks to describe CNS tumors in Canadian infants and to compare their outcomes with those of older children.
Samuel Sassine, Hallie Coltin, Sylvia Cheng, Lucie Lafay‐Cousin, Vanan Issai Magimairajan, Craig Erker, Stéphanie Vairy, Lynette Bowes, Adam Fleming, Donna Johnston, Sarah McKillop, Roona Sinha, Julie Bennett, Christina Coleman, Laura Wheaton, Shayna Zelcer, Samuele Renzi, Thai Hoa Tran   +17 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

Investigating the Reliability and Interpretability of Machine Learning Frameworks for Chemical Retrosynthesis [PDF]

Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability,
Dongda, Zhang, Sophia N., Yaliraki, Friedrich, Hastedt, Ehecatl Antonio, del Rio Chanona, Rowan M., Bailey, Klaus, Hellgardt   +5 more
core   +1 more source