A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation

Zheng, Ervine; Yu, Qi; Li, Rui; Shi, Peng; Haake, Anne R.

doi:10.1609/aaai.v35i7.16752

Cited by 14 publications

(3 citation statements)

References 35 publications

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“…Most research follows regularization-based strategies that calculate the importance of parameters and penalize their deviation [19,30]. Approaches have also been proposed for active learning [31], others allow the storage of previous samples [21,28]. Some methods leverage feature disentanglement to alleviate forgetting [16,18] or maintain task-dependent batch normalization layers [13].…”

Section: Memory Performancementioning

confidence: 99%

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

González¹,

Ranem²,

Othman³

et al. 2022

Preprint

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Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity.

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Section: Memory Performancementioning

confidence: 99%

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

González¹,

Ranem²,

Othman³

et al. 2022

Preprint

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“…Fortunately, uncertainty estimation can be used to gauge model reliability and is already employed extensively in many other applications [22]- [24]. For instance, uncertainty estimation has been widely explored and proven beneficial in various fields such as MRI reconstruction [22], image segmentation [23], and seizure prediction [24]. In these applications, uncertainty estimation not only aids in generating output results but also provides valuable confidence values, enabling better inference by agents.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Denoising Network for Artifact Removal in EEG Signals

Jin,

Wang,

Liu

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The electroencephalogram (EEG) is extensively employed for detecting various brain electrical activities. Nonetheless, EEG recordings are susceptible to undesirable artifacts, resulting in misleading data analysis and even significantly impacting the interpretation of results. While previous efforts to mitigate or reduce the impact of artifacts have achieved commendable performance, several challenges in this domain still persist: 1) Due to black-box skepticism, deep-learning-based automatic EEG artifact removal methods have been impeded from being applied in clinical environments. How to support reliable denoised EEG signals with high accuracy is important. 2) Effectively exploring valuable local and global information from contaminated contexts remains challenging. On the one hand, feature extraction and aggregation in prior works are often performed blindly and assumed to be accurate, which is not always the case. On the other hand, global contextual information is gradually modeled by local fixed singlescaled convolutional filters layer by layer, which is neither efficient nor effective. To address the above challenges, we propose an Uncertainty-aware Denoising Network (UDNet) with multi-scaled pooling attention for efficient context capturing. Specifically, we predict the aleatoric and epistemic uncertainty existing during the denoising process to assist in finding and reducing the uncertain feature representation. We further propose a simple yet effective architecture to capture local and global contexts at multiple scales. Our proposed method can serve as an effective metric for identifying low-confidence epochs that warrant deferral to human experts for further inspection and assessment. Experimental results on two public datasets show that the proposed model outperforms state-of-the-art baselines.

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“…Despite the promise, very few efforts have been made to exploit continual learning in medical settings. Existing work tackles this paradigm in image segmentation [3,9,25,39,40], disease classification [6,18,37], domain adaptation [16] and domain incremental learning [32], showing that they are only approaching a small spectrum of continual learning scenarios. They are bypassing more challenging scenarios, which are already considered for well-curated general imagery datasets and tasks [21].…”

Section: Introductionmentioning

confidence: 99%

LifeLonger: A Benchmark for Continual Disease Classification

Derakhshani

Najdenkoska

Sonsbeek

et al. 2022

Lecture Notes in Computer Science

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Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-theart continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark are publicly available 1 (https://github.com/mmderakhshani/LifeLonger).

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A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation

Cited by 14 publications

References 35 publications

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts

Uncertainty-Aware Denoising Network for Artifact Removal in EEG Signals

LifeLonger: A Benchmark for Continual Disease Classification

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