Deep learning in mechanical metamaterials: from prediction and generation to inverse design
Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …
mechanical properties determined by their microstructures and constituent materials …
A review of building detection from very high resolution optical remote sensing images
Building detection from very high resolution (VHR) optical remote sensing images, which is
an essential but challenging task in remote sensing, has attracted increased attention in …
an essential but challenging task in remote sensing, has attracted increased attention in …
Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease
Plants contribute significantly to the global food supply. Various Plant diseases can result in
production losses, which can be avoided by maintaining vigilance. However, manually …
production losses, which can be avoided by maintaining vigilance. However, manually …
Serial quantitative chest CT assessment of COVID-19: a deep learning approach
Purpose To quantitatively evaluate lung burden changes in patients with coronavirus
disease 2019 (COVID-19) by using serial CT scan by an automated deep learning method …
disease 2019 (COVID-19) by using serial CT scan by an automated deep learning method …
Next-Generation Morphometry for pathomics-data mining in histopathology
Pathology diagnostics relies on the assessment of morphology by trained experts, which
remains subjective and qualitative. Here we developed a framework for large-scale …
remains subjective and qualitative. Here we developed a framework for large-scale …
Samba: Semantic segmentation of remotely sensed images with state space model
High-resolution remotely sensed images pose challenges to traditional semantic
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …
segmentation networks, such as Convolutional Neural Networks (CNNs) and Vision …
Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network
Accurate road detection and centerline extraction from very high resolution (VHR) remote
sensing imagery are of central importance in a wide range of applications. Due to the …
sensing imagery are of central importance in a wide range of applications. Due to the …
A vertebral segmentation dataset with fracture grading
MT Löffler, A Sekuboyina, A Jacob, AL Grau… - Radiology: Artificial …, 2020 - pubs.rsna.org
Keywords: CT, Computer Aided Diagnosis (CAD), Computer Applications-General
(Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks …
(Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks …
[HTML][HTML] Methods for segmentation and classification of digital microscopy tissue images
High-resolution microscopy images of tissue specimens provide detailed information about
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …
the morphology of normal and diseased tissue. Image analysis of tissue morphology can …
Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves
Background Although CT-based body composition (BC) metrics may inform disease risk and
outcomes, obtaining these metrics has been too resource intensive for large-scale use …
outcomes, obtaining these metrics has been too resource intensive for large-scale use …