Active learning using adaptable task-based prioritisation

• URL: http://arxiv.org/abs/2212.01703v1
• Date: Sat, 3 Dec 2022 22:37:38 GMT
• Title: Active learning using adaptable task-based prioritisation
• Authors: Shaheer U. Saeed, Jo\~ao Ramalhinho, Mark Pinnock, Ziyi Shen, Yunguan Fu, Nina Monta\~na-Brown, Ester Bonmati, Dean C. Barratt, Stephen P. Pereira, Brian Davidson, Matthew J. Clarkson, Yipeng Hu
• Abstract summary: We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney.
• Score: 7.0002224852386545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

Related papers

• LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation [5.726588626363204]
  We propose a label-efficient deep learning method with self-ensembling (LESEN) LESEN incorporates supervised and unsupervised losses, enabling the student and teacher models to mutually learn from each other. Our experiments on the human connectome project (HCP) dataset demonstrate the superior performance of our method.
  arXiv  Detail & Related papers  (2024-01-03T10:22:13Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Multi-Level Contrastive Learning for Dense Prediction Task [59.591755258395594]
  We present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. Our method consistently outperforms the recent state-of-the-art methods on various datasets with significant margins.
  arXiv  Detail & Related papers  (2023-04-04T17:59:04Z)
• Classification in Histopathology: A unique deep embeddings extractor for multiple classification tasks [0.0]
  We use a single, pre-trained, deep embeddings extractor to convert images into deep features. We also train small, dedicated classification head on these embeddings for each classification task. This approach offers several benefits such as the ability to reuse a single pre-trained deep network for various tasks.
  arXiv  Detail & Related papers  (2023-03-09T11:19:42Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation [0.16490701092527607]
  We propose an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated.
  arXiv  Detail & Related papers  (2022-07-17T13:28:52Z)
• Selecting task with optimal transport self-supervised learning for few-shot classification [15.088213168796772]
  Few-Shot classification aims at solving problems that only a few samples are available in the training process. We propose a novel task selecting algorithm, named Optimal Transport Task Selecting (OTTS), to construct a training set by selecting similar tasks for Few-Shot learning. OTTS measures the task similarity by calculating the optimal transport distance and completes the model training via a self-supervised strategy.
  arXiv  Detail & Related papers  (2022-04-01T08:45:29Z)
• Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation [6.3858225352615285]
  We explore active learning for the task of segmentation of medical imaging data sets. We propose three new strategies for active learning: increasing frequency of uncertain data to bias the training data set, using mutual information among the input images as a regularizer and adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD) The results indicate an improvement in terms of data reduction by achieving full accuracy while only using 22.69 % and 48.85 % of the available data for each dataset, respectively.
  arXiv  Detail & Related papers  (2021-01-07T01:55:48Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)
• 3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images [58.720142291102135]
  This work investigates the potential of autoencoder-extracted features to improve segmentation with a convolutional neural network. A convolutional autoencoder was used to extract features from unlabeled data and a multi-scale, fully convolutional CNN was used to perform the target task of 3D liver segmentation in CT images.
  arXiv  Detail & Related papers  (2020-03-17T20:20:43Z)

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.