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
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
IntuitionMachines/OrigamiNet
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CVPR 2020
On IAM we even surpass single line methods that use accurate localization information during training.
Learn To Pay Attention
SaoYan/LearnToPayAttention
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ICLR 2018
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
Constrained-CNN losses for weakly supervised segmentation
To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
Integral Object Mining via Online Attention Accumulation
PengtaoJiang/OAA-PyTorch
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ICCV 2019
In order to accumulate the discovered different object parts, we propose an online attention accumulation (OAA) strategy which maintains a cumulative attention map for each target category in each training image so that the integral object regions can be gradually promoted as the training goes.
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
sbelharbi/deep-wsl-histo-min-max-uncertainty
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We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
histocartography/histocartography
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Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
kangningthu/adele
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CVPR 2022
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i. e. predicted label distribution) of a CNN.
