Image segmentation ≈ Image segmentation
View article
Places: A 10 Million Image Database for Scene Recognition Open
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Place…
View article
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Open
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net…
View article
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Open
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extens…
View article
Scene Parsing through ADE20K Dataset Open
Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scen…
View article
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? Open
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a …
View article
Road Extraction by Deep Residual U-Net Open
Road extraction from aerial images has been a hot research topic in the field\nof remote sensing image analysis. In this letter, a semantic segmentation\nneural network which combines the strengths of residual learning and U-Net is\npropos…
View article
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? Open
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has t…
View article
Graph-based approach for airborne light detection and ranging segmentation Open
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, gener…
View article
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications Open
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary …
View article
Mask R-CNN Open
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Th…
View article
Deep Learning Applications in Medical Image Analysis Open
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine …
View article
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Open
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and no…
View article
Color Balance and Fusion for Underwater Image Enhancement Open
We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the…
View article
Detection of plant leaf diseases using image segmentation and soft computing techniques Open
Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper …
View article
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Open
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most med…
View article
The Medical Segmentation Decathlon Open
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organ…
View article
The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks Open
© 2018 IEEE. The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevanc…
View article
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation Open
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety …
View article
U-Prithvi: Integrating a Foundation Model and U-Net for Enhanced Flood Inundation Mapping Open
In recent years, large pre-trained models, commonly referred to as foundation models, have become increasingly popular for various tasks leveraging transfer learning. This trend has expanded to remote sensing, where transformer-based found…
View article
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning Open
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are …
View article
A review of semantic segmentation using deep neural networks Open
During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, …
View article
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl Open
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be s…
View article
Real-Time Scene Text Detection with Differentiable Binarization Open
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. However, the post-processing of binarization is essen…
View article
Recurrent residual U-Net for medical image segmentation Open
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation…
View article
A Review of Deep-Learning-Based Medical Image Segmentation Methods Open
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid d…
View article
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Open
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. Th…
View article
HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation Open
With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. Meanwhile, object detec…
View article
Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods Open
Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of…
View article
A large annotated medical image dataset for the development and evaluation of segmentation algorithms Open
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data …
View article
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation Open
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in…