U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

• URL: http://arxiv.org/abs/2108.04658v1
• Date: Tue, 10 Aug 2021 13:08:39 GMT
• Title: U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations
• Authors: Yichi Zhang, Jesper Kers, Clarissa A. Cassol, Joris J. Roelofs, Najia Idrees, Alik Farber, Samir Haroon, Kevin P. Daly, Suvranu Ganguli, Vipul C. Chitalia, Vijaya B. Kolachalama
• Abstract summary: Deep learning systems for biomedical segmentation often require access to expert-driven, manually annotated datasets. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images.
• Score: 6.682450741319984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared low-dimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (10 patients), and radiologist-driven annotations of lumen cross-sections of human arteriovenous fistulae obtained from intravascular ultrasound images (10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image segmentation.

Related papers

• Multi-rater Prompting for Ambiguous Medical Image Segmentation [12.452584289825849]
  Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters) We propose a multi-rater prompt-based approach to address these two challenges altogether.
  arXiv  Detail & Related papers  (2024-04-11T09:13:50Z)
• Diversified and Personalized Multi-rater Medical Image Segmentation [43.47142636000329]
  We propose a two-stage framework named D-Persona (first Diversification and then Personalization). In Stage I, we exploit multiple given annotations to train a Probabilistic U-Net model, with a bound-constrained loss to improve the prediction diversity. In Stage II, we design multiple attention-based projection heads to adaptively query the corresponding expert prompts from the shared latent space, and then perform the personalized medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-20T09:00:19Z)
• Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks [7.642026462053574]
  We introduce a novel Bayesian neural network-based architecture to estimate inter-rater uncertainty in medical image segmentation. Firstly, we introduce a one-encoder-multi-decoder architecture specifically tailored for uncertainty estimation. Secondly, we propose Bayesian modeling for the new architecture, allowing efficient capture of the inter-rater distribution.
  arXiv  Detail & Related papers  (2023-06-28T20:52:51Z)
• Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models [70.3497683558609]
  A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. We propose a novel method for multi-expert prediction using diffusion models.
  arXiv  Detail & Related papers  (2023-06-15T10:01:05Z)
• 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)
• 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)
• Hierarchical Semantic Segmentation using Psychometric Learning [17.417302703539367]
  We develop a novel approach to collect segmentation annotations from experts based on psychometric testing. Our method consists of the psychometric testing procedure, active query selection, query enhancement, and a deep metric learning model. We show the merits of our method with evaluation on the synthetically generated image, aerial image and histology image.
  arXiv  Detail & Related papers  (2021-07-07T13:38:33Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• 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)
• Unifying Specialist Image Embedding into Universal Image Embedding [84.0039266370785]
  It is desirable to have a universal deep embedding model applicable to various domains of images. We propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem.
  arXiv  Detail & Related papers  (2020-03-08T02:51:11Z)

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.