Results 41 to 50 of about 368,302 (254)
Pediatric Blood &Cancer, EarlyView.ABSTRACT Pediatric radiation therapy presents unique challenges compared to adult treatments, including those of immobilization, potential need for sedation, and the critical importance of accurate, reproducible positioning. Additionally, heightened attention to imaging doses is necessary to minimize long‐term toxicity in survivors.
Parham Alaei +17 more
wiley +1 more source
Topology-Aware Focal Loss for 3D Image Segmentation
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Abstract The efficacy of segmentation algorithms is frequently compromised by topological errors like overlapping regions, disrupted connections, and voids. To tackle this problem, we introduce a novel loss function, namely Topology-Aware Focal Loss (TAFL), that incorporates the conventional Focal Loss with a topological constraint term
Andac Demir +2 more
openaire +2 more sources
Automated Focal Loss for Image based Object Detection [PDF]
2020 IEEE Intelligent Vehicles Symposium (IV), 2020 Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter.
Michael Weber 0009 +2 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT In 2018, the Texas Children's Cancer and Hematology Center Leukemia Program implemented a practice standard to support the transition from treatment to survivorship that includes shared, alternating care between leukemia and survivorship clinicians and a reminder to refer survivors to the long‐term survivor clinic (LTSC) 2 years after ...
Ji Yun Tark +9 more
wiley +1 more source
Personalized Zebrafish Models for Fusion‐Positive Pediatric Sarcomas
Pediatric Blood &Cancer, EarlyView.ABSTRACT Clinical sequencing efforts have revolutionized our approaches to categorizing pediatric cancers in real time. This has dramatically improved our ability to profile pediatric tumors, identify actionable vulnerabilities, and influence clinical care.
Lisa H. Hall +2 more
wiley +1 more source
Revisiting Reweighted Risk for Calibration: AURC, Focal, and Inverse Focal Loss
CoRRSeveral variants of reweighted risk functionals, such as focal loss, inverse focal loss, and the Area Under the Risk Coverage Curve (AURC), have been proposed for improving model calibration; yet their theoretical connections to calibration errors remain under-explored.
Han Zhou 0013 +3 more
openaire +2 more sources
Calibrating Deep Neural Networks using Focal Loss
CoRR, 2020 This paper was accepted at NeurIPS ...
Jishnu Mukhoti +5 more
openaire +4 more sources
Semantic Segmentation Model for Transmission Tower Point Cloud Based on Improved PointNet++
Zhongguo dianli, 2023 Aiming at the existing problem that the point cloud extraction accuracy of transmission lines is not high and cannot meet the needs of autonomous and refined inspection by unmanned aerial vehicles, an improved PointNet++ semantic segmentation method for ...
Zheng HUANG +4 more
doaj +1 more source
The Role of “Adult‐Onset” Cancer Predisposition Genes in Pediatric Cancer: A Comprehensive Review
Pediatric Blood &Cancer, EarlyView.ABSTRACT Current literature estimates that 10% of pediatric cancers are caused by pathogenic or likely pathogenic (P/LP) germline variants in cancer predisposition genes (CPGs). Variants in CPGs thought to increase cancer risk exclusively during adulthood are referred to as “adult‐onset” CPGs (aoCPGs).
Maria Rozo +5 more
wiley +1 more source
An Asymmetric Contrastive Loss for Handling Imbalanced Datasets
Entropy, 2022 Contrastive learning is a representation learning method performed by contrasting a sample to other similar samples so that they are brought closely together, forming clusters in the feature space.
Valentino Vito, Lim Yohanes Stefanus
doaj +1 more source