Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a $(320\times 240)$ image labeling in less than a second, including feature extraction.
Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings
Visualization of boundaries in volumetric data sets through a what material you pick is what boundary you see approach
Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning
A fully automated tortuosity quantification system with application to corneal nerve fibres in confocal microscopy images
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
Application of the PAMONO-Sensor for Quantiﬁcation of Microvesicles and Determination of Nano-Particle Size Distribution
How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View
Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Global detection approach for clustered microcalcifications in mammograms using a deep learning network
Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks
Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis
Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision
A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images
Prediction of Dissolution Data Integrated in Tablet Database Using Four-Layered Artificial Neural Networks
Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm
Intelligent and effective informatic deconvolution of "Big Data" and its future impact on the quantitative nature of neurodegenerative disease therapy
Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning
Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)
Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis
A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma
Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study
Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data
Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks
Active learning strategy and hybrid training for infarct segmentation on diffusion MRI with a U-shaped network
Developing intelligent medical image modality classification system using deep transfer learning and LDA
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
Automatic Identification of Down Syndrome Using Facial Images with Deep Convolutional Neural Network
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
Enhancing the Visibility of Delamination during Pulsed Thermography of Carbon Fiber-Reinforced Plates Using a Stacked Autoencoder
Understanding collective behaviors in reinforcement learning evolutionary games via a belief-based formalization
Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers
Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks
Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology
Multifactorial deep learning reveals pan-cancer genomic tumor clusters with distinct immunogenomic landscape and response to immunotherapy
Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach
Coronaviruses encompass a large family of viruses that cause the common cold as well as more serious diseases, such as the ongoing outbreak of coronavirus disease 2019 (COVID-19; formally known as 2019-nCoV). Coronaviruses can spread from animals to humans; symptoms include fever, cough, shortness of breath, and breathing difficulties; in more severe cases, infection can lead to death. This feed covers recent research on COVID-19.
Chronic Fatigue Syndrome
Chronic fatigue syndrome is a disease characterized by unexplained disabling fatigue; the pathology of which is incompletely understood. Discover the latest research on chronic fatigue syndrome here.
Systemic Juvenile Idiopathic Arthritis
Systemic juvenile idiopathic arthritis is a rare rheumatic disease that affects children. Symptoms include joint pain, but also fevers and skin rashes. Here is the latest on this disease.
Chromatin Regulation and Circadian Clocks
The circadian clock plays an important role in regulating transcriptional dynamics through changes in chromatin folding and remodelling. Discover the latest research on Chromatin Regulation and Circadian Clocks here.
Central Pontine Myelinolysis
Central Pontine Myelinolysis is a neurologic disorder caused most frequently by rapid correction of hyponatremia and is characterized by demyelination that affects the central portion of the base of the pons. Here is the latest research on this disease.
Myocardial stunning is a mechanical dysfunction that persists after reperfusion of previously ischemic tissue in the absence of irreversible damage including myocardial necrosis. Here is the latest research.
Pontocerebellar hypoplasias are a group of neurodegenerative autosomal recessive disorders with prenatal onset, atrophy or hypoplasia of the cerebellum, hypoplasia of the ventral pons, microcephaly, variable neocortical atrophy and severe mental and motor impairments. Here is the latest research on pontocerebellar hypoplasia.
Cell Atlas Along the Gut-Brain Axis
Profiling cells along the gut-brain axis at the single cell level will provide unique information for each cell type, a three-dimensional map of how cell types work together to form tissues, and insights into how changes in the map underlie health and disease of the GI system and its crosstalk with the brain. Disocver the latest research on single cell analysis of the gut-brain axis here.
Chronic Traumatic Encephalopathy
Chronic Traumatic Encephalopathy (CTE) is a progressive degenerative disease that occurs in individuals that suffer repetitive brain trauma. Discover the latest research on traumatic encephalopathy here.