Few-shot Class-incremental Learning for Cross-domain Disease Classification

• URL: http://arxiv.org/abs/2304.05734v1
• Date: Wed, 12 Apr 2023 09:43:39 GMT
• Title: Few-shot Class-incremental Learning for Cross-domain Disease Classification
• Authors: Hao Yang, Weijian Huang, Jiarun Liu, Cheng Li, Shanshan Wang
• Abstract summary: We explore the cross-domain few-shot incremental learning problem. We propose a cross-domain enhancement constraint and cross-domain data augmentation method. Experiments on MedMNIST show that the classification performance of this method is better than other similar incremental learning methods.
• Score: 20.194071205063153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application. Although existing incremental learning techniques have attempted to address this issue, they still struggle with only few labeled data, particularly when the samples are from varied domains. In this paper, we explore the cross-domain few-shot incremental learning (CDFSCIL) problem. CDFSCIL requires models to learn new classes from very few labeled samples incrementally, and the new classes may be vastly different from the target space. To counteract this difficulty, we propose a cross-domain enhancement constraint and cross-domain data augmentation method. Experiments on MedMNIST show that the classification performance of this method is better than other similar incremental learning methods.

Related papers

• CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning [52.63674911541416]
  Few-shot class-incremental learning (FSCIL) faces several challenges, such as overfitting and forgetting. Our primary focus is representation learning on base classes to tackle the unique challenge of FSCIL. We find that trying to secure the spread of features within a more confined feature space enables the learned representation to strike a better balance between transferability and discriminability.
  arXiv  Detail & Related papers  (2024-10-08T02:23:16Z)
• Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark [47.52603262576663]
  We propose a novel benchmark combining the challenges of new class arrivals and domain shifts in a single framework. This benchmark aims to model a realistic CL setting for the multi-label classification problem in medical imaging.
  arXiv  Detail & Related papers  (2024-04-10T09:35:36Z)
• Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning [99.14132861655223]
  We propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining. Our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%.
  arXiv  Detail & Related papers  (2022-11-29T14:52:38Z)
• Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations [22.289830907729705]
  Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one. Main challenge comes from the "catastrophic forgetting" issue -- the inability to well remember the learnt knowledge while learning the new ones.
  arXiv  Detail & Related papers  (2022-11-10T05:29:43Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Towards Generalized and Incremental Few-Shot Object Detection [9.033533653482529]
  A novel Incremental Few-Shot Object Detection (iFSOD) method is proposed to enable the effective continual learning from few-shot samples. Specifically, a Double-Branch Framework (DBF) is proposed to decouple the feature representation of base and novel (few-shot) class. We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection.
  arXiv  Detail & Related papers  (2021-09-23T12:38:09Z)
• Class-Incremental Domain Adaptation [56.72064953133832]
  We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA) Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Our approach yields superior performance as compared to both DA and CI methods in the CIDA paradigm.
  arXiv  Detail & Related papers  (2020-08-04T07:55:03Z)
• Few-Shot Class-Incremental Learning [68.75462849428196]
  We focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. We represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes.
  arXiv  Detail & Related papers  (2020-04-23T03:38:33Z)

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.