Results 81 to 90 of about 1,916,086 (289)

The Role of Invasive Procedures in the Treatment of Complicated Gastrointestinal Graft‐Versus‐Host Disease in Pediatric Patients

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Gastrointestinal graft‐versus‐host disease (GI GVHD) following hematopoietic stem cell transplant is typically managed with medical therapy, but surgery and angioembolization may be warranted in selected cases with life‐threatening complications.
Gaia Brunetti   +12 more
wiley   +1 more source

Tiny Object Detection via Normalized Gaussian Label Assignment and Multi-Scale Hybrid Attention

open access: yesRemote Sensing
The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing ...
Shihao Lin   +3 more
doaj   +1 more source

CNN Features off-the-shelf: an Astounding Baseline for Recognition

open access: yes, 2014
Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case.
Azizpour, Hossein   +3 more
core   +1 more source

Two Faces of NOTCH1 in Childhood Lymphoblastic T‐Cell Neoplasia: Prognostic Divergence of Mutational and Structural Aberrations

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT In pediatric patients, T‐cell lymphoblastic lymphoma (T‐LBL) survival exceeds 80%. Relapse remains associated with limited curative options. Frontline treatment is largely extrapolated from T‐cell acute lymphoblastic leukemia (T‐ALL) treatment, reflecting the ongoing debate, whether both entities represent distinct diseases or variants within ...
Marie C. Heider   +4 more
wiley   +1 more source

Recognizing and Curating Photo Albums via Event-Specific Image Importance

open access: yes, 2017
Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection.
Cottrell, Garrison W.   +5 more
core   +1 more source

Low‐Power Image Recognition Challenge

open access: yesAI Magazine, 2018
The Low‐Power Image Recognition Challenge (LPIRC) has been held annually since 2015. This article summarizes the competition advancements made over the past three years.
Lu, Yung-Hsiang   +2 more
openaire   +3 more sources

Blinatumomab Utilization in Pediatric B‐Cell Acute Lymphoblastic Leukemia: Experience From the Mountain West

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Blinatumomab is a bispecific T‐cell engager approved for the treatment of pediatric B‐cell acute lymphoblastic leukemia (B‐ALL). Outpatient home infusion reduces hospitalization burden and optimizes resource utilization, but is logistically challenging.
Angela Parra del Riego   +10 more
wiley   +1 more source

Assessing Cognitive Functioning in Children With Brain Tumors: Interaction of Neighborhood Social Determinants of Health and Neurological Risk

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background This study investigated how neighborhood‐level social determinants of health (SDOH), including redlining and neurological risk, interact to influence cognitive outcomes in children treated for brain tumors (CTBT). Methods A retrospective chart review of 161 CTBT aged 5–17 was conducted.
Alannah R. Srsich   +5 more
wiley   +1 more source

Double-Channel Object Tracking With Position Deviation Suppression

open access: yesIEEE Access, 2020
The object tracking methods based on multi-domain convolutional neural network (MDNet) commonly fail to track in the case of background clutter. A novel double-channel object tracking (DCOT) is proposed to solve this problem.
Jun Chu, Xuji Tu, Lu Leng, Jun Miao
doaj   +1 more source

Visual attention models for scene text recognition

open access: yes, 2017
In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features.
Bagdanov, Andrew D.   +2 more
core   +1 more source

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