Weakly supervised segmentation
64 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Weakly supervised segmentation
ACDC Scribbles
MARIDA
CheXlocalize
Most implemented papers
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
cq615/Joint-Motion-Estimation-and-Segmentation
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Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
js-fan/CIAN
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Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels.
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
LIVIAETS/extended_logbarrier
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While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables.
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation
SSNet consists of a segmentation network (SN) and a saliency aggregation module (SAM).
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans.
Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation
shimoda-uec/ssdd
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ICCV 2019
In this paper, to make the most of such mapping functions, we assume that the results of the mapping function include noise, and we improve the accuracy by removing noise.
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
LIVIAETS/boxes_tightness_prior
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MIDL 2019
Particularly, we leverage a classical tightness prior to a deep learning setting via imposing a set of constraints on the network outputs.
ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation
In the new paradigm, prior knowledge is encoded and depicted by reference masks, and is further employed to impose constraints on segmentation outputs through adversarial learning with reference masks.
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
ucuapps/WSMIS
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First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure.
