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Found 14 papers, 10 papers with code
Leveraging point annotations in segmentation learning with boundary loss
Hence it is intuitively inappropriate for weak supervision, where the ground truth label may be much smaller than the actual object and a certain amount of false positives (w. r. t.
On the dice loss gradient and the ways to mimic it
In the past few years, in the context of fully-supervised semantic segmentation, several losses -- such as cross-entropy and dice -- have emerged as de facto standards to supervise neural networks.
Source-Free Domain Adaptation for Image Segmentation
Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
We also found that shape descriptors can be a valid way to encode anatomical priors about the task, enabling to leverage expert knowledge without additional annotations.
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm.
Ranked #3 on
Few-Shot Semantic Segmentation
on COCO-20i (10-shot)
Source-Relaxed Domain Adaptation for Image Segmentation
Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions.
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
Particularly, we leverage a classical tightness prior to a deep learning setting via imposing a set of constraints on the network outputs.
Laplacian pyramid-based complex neural network learning for fast MR imaging
A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data.
Discretely-constrained deep network for weakly supervised segmentation
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions.
Constrained domain adaptation for Image segmentation
We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region.
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
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.
Boundary loss for highly unbalanced segmentation
We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.
Brain Lesion Segmentation From Mri
Ischemic Stroke Lesion Segmentation
+4
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.
