Results 51 to 60 of about 1,984,141 (275)
Revisiting IM2GPS in the Deep Learning Era
Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problem is a deep image classification approach in which the ...
Hays, James, Jacobs, Nathan, Vo, Nam
core +1 more source
ABSTRACT Primary lung carcinomas and bronchial carcinoid tumors (BC) are very rare malignancies in childhood. While typical BC and mucoepidermoid carcinomas are mostly low‐grade, localized tumors with a more favorable prognosis than in adults, necessitating avoidance of overtreatment, adenocarcinomas of the lung are often diagnosed at advanced disease ...
Michael Abele +19 more
wiley +1 more source
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification.
Aggarwal, Charu +3 more
core +1 more source
ABSTRACT Pediatric gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are extremely rare and clinically heterogeneous. Management has largely been extrapolated from adult practice. This European Standard Clinical Practice Guideline (ESCP), developed by the EXPeRT network in collaboration with adult NEN experts, provides (adult) evidence ...
Michaela Kuhlen +23 more
wiley +1 more source
ABSTRACT Background 131I‐metaiodobenzylguanidine (131I‐MIBG) radiotherapy is a key treatment for relapsed and refractory (R/R) neuroblastoma (NB). Patients with R/R disease treated in the modern era are increasingly exposed to anti‐GD2 immunotherapy, which exerts selective pressure and may modify both tumor cell state and microenvironment.
Benjamin J. Lerman +7 more
wiley +1 more source
A Review of Image Classification Algorithms in IoT
With the advent of big data era and the enhancement of computing power, Deep Learning has swept the world. Based on Convolutional Neural Network (CNN) image classification technique broke the restriction of classical image classification methods ...
Xiaopeng Zheng, Rayan S Cloutier
doaj +1 more source
Between-class Learning for Image Classification
In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio.
Harada, Tatsuya +2 more
core +1 more source
ABSTRACT Claudin‐6 has emerged as a promising immunotherapeutic target, yet protein‐level data in atypical teratoid/rhabdoid tumors (AT/RTs) have been inconsistent. We analyzed 36 well‐characterized AT/RT samples and found membranous claudin‐6 protein expression in 58% of cases, with striking enrichment in the molecular subgroup AT/RT‐TYR (100%) and ...
Victoria E. Fincke +4 more
wiley +1 more source
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is
Jiang, Mengqing +7 more
core +1 more source
ABSTRACT Background Wilms tumor (WT) treatment imposes a significant time burden on patients and their families. Time toxicity is a patient‐centered metric that quantifies the burden of healthcare interaction. We sought to define time toxicity in the first year after diagnosis of WT and hypothesized that it would increase as tumor stage and treatment ...
Caleb Q. Ashbrook +6 more
wiley +1 more source

