Disentangling Human Error from the Ground Truth in Segmentation of
Medical Images
- URL: http://arxiv.org/abs/2007.15963v5
- Date: Fri, 23 Oct 2020 12:15:04 GMT
- Title: Disentangling Human Error from the Ground Truth in Segmentation of
Medical Images
- Authors: Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga
Ciccarelli, Frederik Barkhof and Daniel C. Alexander
- Abstract summary: We present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions.
We demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations.
- Score: 12.009437407687987
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent years have seen increasing use of supervised learning methods for
segmentation tasks. However, the predictive performance of these algorithms
depends on the quality of labels. This problem is particularly pertinent in the
medical image domain, where both the annotation cost and inter-observer
variability are high. In a typical label acquisition process, different human
experts provide their estimates of the "true" segmentation labels under the
influence of their own biases and competence levels. Treating these noisy
labels blindly as the ground truth limits the performance that automatic
segmentation algorithms can achieve. In this work, we present a method for
jointly learning, from purely noisy observations alone, the reliability of
individual annotators and the true segmentation label distributions, using two
coupled CNNs. The separation of the two is achieved by encouraging the
estimated annotators to be maximally unreliable while achieving high fidelity
with the noisy training data. We first define a toy segmentation dataset based
on MNIST and study the properties of the proposed algorithm. We then
demonstrate the utility of the method on three public medical imaging
segmentation datasets with simulated (when necessary) and real diverse
annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours);
3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms
competing methods and relevant baselines particularly in cases where the number
of annotations is small and the amount of disagreement is large. The
experiments also show strong ability to capture the complex spatial
characteristics of annotators' mistakes.
Related papers
- Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning [3.231698506153459]
Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data.
Such labels are typically highly time-consuming, error-prone and expensive to produce.
Semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled.
arXiv Detail & Related papers (2024-04-02T09:31:06Z) - Multi-task Explainable Skin Lesion Classification [54.76511683427566]
We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data.
The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
arXiv Detail & Related papers (2023-10-11T05:49:47Z) - Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object
Detection with Repeated Labels [6.872072177648135]
We propose a novel localization algorithm that adapts well-established ground truth estimation methods.
Our algorithm also shows superior performance during training on the TexBiG dataset.
arXiv Detail & Related papers (2023-09-18T13:08:44Z) - 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) - 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) - 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) - Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray
Images [0.0]
We propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision.
We show that this approach is applicable to chest X-rays for detecting an anomalous volume of air between the lung and the chest wall.
arXiv Detail & Related papers (2020-07-01T20:48:35Z) - ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised
Medical Image Segmentation [99.90263375737362]
We propose ATSO, an asynchronous version of teacher-student optimization.
ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset.
We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
arXiv Detail & Related papers (2020-06-24T04:05:12Z) - Semi-supervised Medical Image Classification with Relation-driven
Self-ensembling Model [71.80319052891817]
We present a relation-driven semi-supervised framework for medical image classification.
It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations.
Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
arXiv Detail & Related papers (2020-05-15T06:57:54Z) - Semi-supervised lung nodule retrieval [2.055949720959582]
A content based image retrieval (CBIR) system provides as its output a set of images, ranked by similarity to the query image.
Ground truth on similarity between dataset elements (e.g. between nodules) is not readily available, thus greatly challenging machine learning methods.
The current study suggests a semi-supervised approach that involves two steps: 1) Automatic annotation of a given partially labeled dataset; 2) Learning a semantic similarity metric space based on the predicated annotations.
The proposed system is demonstrated in lung nodule retrieval using the LIDC dataset, and shows that it is feasible to learn embedding from predicted ratings.
arXiv Detail & Related papers (2020-05-04T19:26:14Z)
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.