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Top News In Image Segmentation Today - Breaking & Trending Today

@imgly/background-removal-node

Background Removal in NodeJS. Latest version: 1.1.5, last published: 8 days ago. Start using @imgly/background-removal-node in your project by running `npm i @imgly/background-removal-node`. There are no other projects in the npm registry using @imgly/background-removal-node. ....

Imagedata Arraybuffer Uint , Arraybuffer Uint , Neural Network , Background Removal , Custom Asset Serving , Image Segmentation , Image Matting ,

DDN Storage Solutions Deliver 700% Gains in AI and Machine Learning for Image Segmentation and Natural Language Processing

/PRNewswire/ DDN®, the global leader in artificial intelligence (AI) and multi-cloud data management solutions, today announced impressive performance. ....

United States , James Coomer , Sharon Sumrit , Mlcommons Association , Walt Company , From Cancer Detection , Sentiment Analysis , Power Efficient , Image Segmentation , Natural Language Processing , Large Language Models , Datadirect Networks Ddn ,

DDN Shows Strong Results in Inaugural MLPerf AI Storage Benchmark

DDN Shows Strong Results in Inaugural MLPerf AI Storage Benchmark
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James Coomer , Mlcommons Association , Image Segmentation , Natural Language Processing , Large Language Models ,

"AdaptorNAS: A New Perturbation-based Neural Architecture Search for Hy" by Sui Paul Ang, Son Lam Phung et al.

Hyperspectral image segmentation is an emerging area with numerous applications, including agriculture, forestry, environment monitoring, and remote sensing. This paper proposes a new neural architecture search algorithm, named AdaptorNAS, for hyperspectral image segmentation. AdaptorNAS aims to design the optimum decoder for any given encoder. In our approach, the search space of AdaptorNAS is a large deep neural network (DNN), and the optimal decoder is derived by pruning the large DNN via a perturbation-based pruning strategy. Verified on three popular encoders, i.e., ResNet-34, MobileNet-V2, and EfficientNet-B2, AdaptorNAS can design high-speed decoders that are significantly better than six common hand-crafted decoders. Additionally, with the EfficientNet-B2 encoder, AdaptorNAS (mIoU of 92.47% and mDice of 95.15%) outperforms the state-of-the-art NAS algorithms and hand-crafted network architectures on the hyperspectral image segmentation task. We also introduce a new hyperspectra ....

Biosecurity Scanning , Computer Architecture , Deep Learning , Feature Extraction , Yperspectral Image Segmentation , Hyperspectral Imaging , Image Segmentation , Neural Architecture Search , Erturbation Based Search , Semantic Segmentation ,

"Automated detection, delineation and quantification of whole-body bone" by R. Nigam, M. Field et al.

Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, de ....

Bone Metastases , Image Segmentation , Multimodality Imaging , Adiotherapy Treatment Planning , Stereotactic Body Radiotherapy , Umour Delineation ,