Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization

• URL: http://arxiv.org/abs/2103.04813v1
• Date: Mon, 8 Mar 2021 15:13:25 GMT
• Title: Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization
• Authors: Jizong Peng and Marco Pedersoli and Christian Desrosiers
• Abstract summary: We present a novel semi-supervised segmentation method that leverages mutual information (MI) on categorical distributions. We evaluate the method on three challenging publicly-available datasets for medical image segmentation.
• Score: 9.994508738317585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images. Semi-supervised learning seeks to overcome this limitation by leveraging unlabeled examples in the learning process. In this paper, we present a novel semi-supervised segmentation method that leverages mutual information (MI) on categorical distributions to achieve both global representation invariance and local smoothness. In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network. We first propose a global MI loss constraining the encoder to learn an image representation that is invariant to geometric transformations. Instead of resorting to computationally-expensive techniques for estimating the MI on continuous feature embeddings, we use projection heads to map them to a discrete cluster assignment where MI can be computed efficiently. Our method also includes a local MI loss to promote spatial consistency in the feature maps of the decoder and provide a smoother segmentation. Since mutual information does not require a strict ordering of clusters in two different assignments, we incorporate a final consistency regularization loss on the output which helps align the cluster labels throughout the network. We evaluate the method on three challenging publicly-available datasets for medical image segmentation. Experimental results show our method to outperform recently-proposed approaches for semi-supervised segmentation and provide an accuracy near to full supervision while training with very few annotated images

Related papers

• SegMatch: A semi-supervised learning method for surgical instrument segmentation [10.223709180135419]
  We propose SegMatch, a semi supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi supervised classification pipeline combining consistency regularization and pseudo labelling. Our results demonstrate that adding unlabelled data for training purposes allows us to surpass the performance of fully supervised approaches.
  arXiv  Detail & Related papers  (2023-08-09T21:30:18Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation [3.6748639131154315]
  We extend the concept of metric learning to the segmentation task. We propose a simple convolutional projection head for obtaining dense pixel-level features. A bidirectional regularization mechanism involving two-stream regularization training is devised for the downstream task.
  arXiv  Detail & Related papers  (2022-10-26T23:11:02Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation [0.0]
  The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. We introduce a novel regularization strategy involving computation-based mixing for semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2022-02-01T16:21:14Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization [7.165364364478119]
  We propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adrial Regularization (InMARS) Inspired by human perception which parses a scene into perceptual groups, our proposed approach first partitions an input image into meaningful regions (also known as superpixels) Next, it utilizes Mutual-Information-Maximization followed by an adversarial training strategy to cluster these regions into semantically meaningful classes. Our experiments demonstrate that our method achieves the state-of-the-art performance on two commonly used unsupervised semantic segmentation datasets.
  arXiv  Detail & Related papers  (2021-07-01T18:36:27Z)
• Semi-supervised Left Atrium Segmentation with Mutual Consistency Training [60.59108570938163]
  We propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively.
  arXiv  Detail & Related papers  (2021-03-04T09:34:32Z)
• Semi-supervised Medical Image Segmentation through Dual-task Consistency [18.18484640332254]
  We propose a novel dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target. Our method can largely improve the performance by incorporating the unlabeled data. Our framework outperforms the state-of-the-art semi-supervised medical image segmentation methods.
  arXiv  Detail & Related papers  (2020-09-09T17:49:21Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation [93.83369981759996]
  We propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation. We propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning.
  arXiv  Detail & Related papers  (2020-04-09T14:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.