Results 81 to 90 of about 788,437 (264)
Deep Residual Learning for Image Recognition
, 2015 Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
He, Kaiming +3 more
core +1 more source
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 more
wiley +1 more source
Residual Learning Capability in Organic Amnesia
Cortex, 1982 This article provides a review of residual learning capability in patients suffering from the organic amnesic syndrome. It is shown that organic amnesics are able to learn a considerable number of laboratory tasks, many to a level comparable with normals.
openaire +2 more sources
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
Building extraction from remote sensing images using deep residual U-Net
European Journal of Remote Sensing, 2022 Building extraction is a fundamental area of research in the field of remote sensing. In this paper, we propose an efficient model called residual U-Net (RU-Net) to extract buildings. It combines the advantages of U-Net, residual learning, atrous spatial
Haiying Wang, Fang Miao
doaj +1 more source
Deep residual learning in CT physics: scatter correction for spectral CT
, 2018 Recently, spectral CT has been drawing a lot of attention in a variety of clinical applications primarily due to its capability of providing quantitative information about material properties.
Manjeshwar, Ravindra +3 more
core +1 more source
Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
wiley +1 more source
GAN-Based Rain Noise Removal From Single-Image Considering Rain Composite Models
IEEE Access, 2020 Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. For example, on a rainy day, autonomous vehicles have difficulty determining how to navigate due to the degraded visual quality of images.
Takuro Matsui, Masaaki Ikehara
doaj +1 more source
Residual Attention Network for Image Classification
, 2017 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
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source