Continual learning in medical imaging analysis: A comprehensive review of recent advancements and future prospects
Medical imaging analysis has witnessed remarkable advancements even surpassing
human-level performance in recent years, driven by the rapid development of advanced …
human-level performance in recent years, driven by the rapid development of advanced …
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis
Whole slide image (WSI) analysis has become increasingly important in the medical
imaging community, enabling automated and objective diagnosis, prognosis, and …
imaging community, enabling automated and objective diagnosis, prognosis, and …
Multimedia datasets for anomaly detection: a review
Multimedia anomaly datasets play a crucial role in automated surveillance. They have a
wide range of applications expanding from outlier objects/situation detection to the detection …
wide range of applications expanding from outlier objects/situation detection to the detection …
Revisiting distillation for continual learning on visual question localized-answering in robotic surgery
The visual-question localized-answering (VQLA) system can serve as a knowledgeable
assistant in surgical education. Except for providing text-based answers, the VQLA system …
assistant in surgical education. Except for providing text-based answers, the VQLA system …
Fs3dciot: A few-shot incremental learning network for skin disease differential diagnosis in the consumer iot
J Xiao, J Li, H Gao - IEEE Transactions on Consumer …, 2023 - ieeexplore.ieee.org
The computer-aided diagnosis (CAD) method based on few-shot learning (FSL) effectively
reduces the dependence on labelled medical images. However, the catastrophic forgetting …
reduces the dependence on labelled medical images. However, the catastrophic forgetting …
Does continual learning equally forget all parameters?
Distribution shift (eg, task or domain shift) in continual learning (CL) usually results in
catastrophic forgetting of previously learned knowledge. Although it can be alleviated by …
catastrophic forgetting of previously learned knowledge. Although it can be alleviated by …
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
The performance of a lifelong learning (L3) model degrades when it is trained on a series of
tasks, as the geometrical formation of the embedding space changes while learning novel …
tasks, as the geometrical formation of the embedding space changes while learning novel …
A continual learning approach for cross-domain white blood cell classification
Accurate classification of white blood cells in peripheral blood is essential for diagnosing
hematological diseases. Due to constantly evolving clinical settings, data sources, and …
hematological diseases. Due to constantly evolving clinical settings, data sources, and …
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …
analysis. However, despite these achievements, the further enhancement of deep learning …
Privacy-preserving continual learning methods for medical image classification: a comparative analysis
Background The implementation of deep learning models for medical image classification
poses significant challenges, including gradual performance degradation and limited …
poses significant challenges, including gradual performance degradation and limited …