Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net

• URL: http://arxiv.org/abs/2205.05293v1
• Date: Wed, 11 May 2022 06:42:24 GMT
• Title: Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net
• Authors: Risako Tanigawa, Yasunori Ishii, Kazuki Kozuka and Takayoshi Yamashita
• Abstract summary: We propose a new task for human segmentation from invisible information, especially airborne ultrasound. Although ultrasound images can roughly identify a person's location, the detailed shape is ambiguous. We propose a collaborative learning probabilistic U-Net that uses ultrasound and segmentation images simultaneously during training.
• Score: 8.21448246263952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Color images are easy to understand visually and can acquire a great deal of information, such as color and texture. They are highly and widely used in tasks such as segmentation. On the other hand, in indoor person segmentation, it is necessary to collect person data considering privacy. We propose a new task for human segmentation from invisible information, especially airborne ultrasound. We first convert ultrasound waves to reflected ultrasound directional images (ultrasound images) to perform segmentation from invisible information. Although ultrasound images can roughly identify a person's location, the detailed shape is ambiguous. To address this problem, we propose a collaborative learning probabilistic U-Net that uses ultrasound and segmentation images simultaneously during training, closing the probabilistic distributions between ultrasound and segmentation images by comparing the parameters of the latent spaces. In inference, only ultrasound images can be used to obtain segmentation results. As a result of performance verification, the proposed method could estimate human segmentations more accurately than conventional probabilistic U-Net and other variational autoencoder models.

Related papers

• Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation [53.37499744840018]
  This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset.
  arXiv  Detail & Related papers  (2024-08-04T14:30:14Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• LOTUS: Learning to Optimize Task-based US representations [39.81131738128329]
  Anatomical segmentation of organs in ultrasound images is essential to many clinical applications. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. In this paper, we propose a novel approach for learning to optimize task-based ultra-sound image representations.
  arXiv  Detail & Related papers  (2023-07-29T16:29:39Z)
• Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder [8.21448246263952]
  We propose a new task for human instance segmentation from invisible information, especially airborne ultrasound, for action recognition. To perform instance segmentation from invisible information, we first convert sound waves to reflected sound directional images (sound images) In inference, it is possible to obtain instance segmentation results only from sound images.
  arXiv  Detail & Related papers  (2022-04-15T00:56:01Z)
• Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks [48.732863591145964]
  We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
  arXiv  Detail & Related papers  (2021-10-12T21:22:24Z)
• Ultrasound Image Classification using ACGAN with Small Training Dataset [0.0]
  Training deep learning models requires large labeled datasets, which is often unavailable for ultrasound images. We exploit Generative Adversarial Network (ACGAN) that combines the benefits of large data augmentation and transfer learning. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2021-01-31T11:11:24Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)
• DenoiSeg: Joint Denoising and Segmentation [75.91760529986958]
  We propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations. We achieve this by extending Noise2Void, a self-supervised denoising scheme that can be trained on noisy images alone, to also predict dense 3-class segmentations.
  arXiv  Detail & Related papers  (2020-05-06T17:42:54Z)
• Breast lesion segmentation in ultrasound images with limited annotated data [2.905751301655124]
  We propose the use of simulated US images and natural images as auxiliary datasets in order to pre-train our segmentation network. We show that fine-tuning the pre-trained network improves the dice score by 21% compared to training from scratch.
  arXiv  Detail & Related papers  (2020-01-21T03:34:42Z)

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.