A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
Fully Convolutional Networks for Semantic Segmentation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.
fields
cs.CV 3representative citing papers
SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
A pix2pix GAN is used for autonomous segmentation of epicardial and mediastinal cardiac fat from CT images, reporting average accuracies of 99.08% and 97.90% with f1-scores above 98% and real-time performance.
citing papers explorer
-
U-Net: Convolutional Networks for Biomedical Image Segmentation
A u-shaped fully-convolutional encoder-decoder with skip connections trained with elastic-deformation augmentation produces accurate biomedical image segmentations from very small training sets.
-
SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation
SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
-
Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network
A pix2pix GAN is used for autonomous segmentation of epicardial and mediastinal cardiac fat from CT images, reporting average accuracies of 99.08% and 97.90% with f1-scores above 98% and real-time performance.