User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

• URL: http://arxiv.org/abs/2009.02455v1
• Date: Sat, 5 Sep 2020 04:24:58 GMT
• Title: User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
• Authors: Ashwin Raju, Zhanghexuan Ji, Chi Tung Cheng, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHung Liao, Adam P. Harrison
• Abstract summary: Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. We propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. We show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs.
• Score: 49.96706092808873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of 96.1%(94.9%), compared to 93.0% (87.0%) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.

Related papers

• Bridge the Points: Graph-based Few-shot Segment Anything Semantically [79.1519244940518]
  Recent advancements in pre-training techniques have enhanced the capabilities of vision foundation models. Recent studies extend the SAM to Few-shot Semantic segmentation (FSS) We propose a simple yet effective approach based on graph analysis.
  arXiv  Detail & Related papers  (2024-10-09T15:02:28Z)
• Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI [11.871709357017416]
  Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. We propose an ASD feature reduction pipeline using resting-state fMRI (rs-fMRI) We used Ncuts parcellations and Power atlas to extract functional connectivity data, resulting in over 30 thousand features.
  arXiv  Detail & Related papers  (2024-09-30T09:38:47Z)
• Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach [0.0]
  We propose a triplet encoder and single decoder network based on U-Net, tU-Net (triplet U-Net) Our proposed architecture is able to exploit non-annotated sagittal and coronal views via contrastive learning to improve the segmentation from a volumetric perspective.
  arXiv  Detail & Related papers  (2023-08-12T06:07:47Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation [104.40114562948428]
  In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. We propose a Masked Image Consistency (MIC) module to enhance UDA by learning spatial context relations of the target domain. MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA.
  arXiv  Detail & Related papers  (2022-12-02T17:29:32Z)
• Adaptive Graph-Based Feature Normalization for Facial Expression Recognition [1.2246649738388389]
  We propose an Adaptive Graph-based Feature Normalization (AGFN) method to protect Facial Expression Recognition models from data uncertainties. Our method outperforms state-of-the-art works with accuracies of 91.84% and 91.11% on benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-22T14:57:56Z)
• Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs [19.09848738521126]
  Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. We present two interface modules to facilitate a more intuitive assessment of model reliability.
  arXiv  Detail & Related papers  (2021-02-17T02:41:32Z)
• Understanding Visual Saliency in Mobile User Interfaces [31.278845008743698]
  We present findings from a controlled study with 30 participants and 193 mobile UIs. Results speak to a role of expectations in guiding where users look at. We release the first annotated dataset for investigating visual saliency in mobile UIs.
  arXiv  Detail & Related papers  (2021-01-22T15:45:13Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.