# LifeLonger: A Benchmark for Continual Disease Classification

arXiv: https://arxiv.org/abs/2204.05737 · HTML: https://mlsanity.com/paper/2204.05737/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning](https://mlsanity.com/paper/1904.03137/) (2019)
- [FearNet: Brain-Inspired Model for Incremental Learning](https://mlsanity.com/paper/1711.10563/) (2017)
- [Avalanche: an End-to-End Library for Continual Learning](https://mlsanity.com/paper/2104.00405/) (2021)
- [Continual Learning for Domain Adaptation in Chest X-ray Classification](https://mlsanity.com/paper/2001.05922/) (2020)
- [Kernel Continual Learning](https://mlsanity.com/paper/2107.05757/) (2021)
- [What is Wrong with Continual Learning in Medical Image Segmentation?](https://mlsanity.com/paper/2010.11008/) (2020)
- [Towards continual learning in medical imaging](https://mlsanity.com/paper/1811.02496/) (2018)
- [Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation](https://mlsanity.com/paper/2107.08751/) (2021)

## Built on by (most cited first, 28 of 28)

- [ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis](https://mlsanity.com/paper/2308.13324/) (2023)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Does Continual Learning Equally Forget All Parameters?](https://mlsanity.com/paper/2304.04158/) (2023)
- [Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.12045/) (2023)
- [A Continual Learning Approach for Cross-Domain White Blood Cell Classification](https://mlsanity.com/paper/2308.12679/) (2023)
- [Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification](https://mlsanity.com/paper/2410.10573/) (2024)
- [Continual Learning in Medical Imaging: A Survey and Practical Analysis](https://mlsanity.com/paper/2405.13482/) (2024)
- [L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space](https://mlsanity.com/paper/2307.16459/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Image Distillation for Safe Data Sharing in Histopathology](https://mlsanity.com/paper/2406.13536/) (2024)
- [FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging](https://mlsanity.com/paper/2407.08822/) (2024)
- [Continual Domain Incremental Learning for Privacy-aware Digital Pathology](https://mlsanity.com/paper/2409.06455/) (2024)
- [Few-shot Class-incremental Learning for Cross-domain Disease Classification](https://mlsanity.com/paper/2304.05734/) (2023)
- [Characterizing Continual Learning Scenarios and Strategies for Audio Analysis](https://mlsanity.com/paper/2407.00465/) (2024)
- [Fairness Evolution in Continual Learning for Medical Imaging](https://mlsanity.com/paper/2406.02480/) (2024)
- [CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning](https://mlsanity.com/paper/2406.05631/) (2024)
- [NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation](https://mlsanity.com/paper/2410.23368/) (2024)
- [Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis](https://mlsanity.com/paper/2505.08524/) (2025)
- [DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2508.09785/) (2025)
- [From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion](https://mlsanity.com/paper/2409.07757/) (2024)
- [Confidence-Based Task Prediction in Continual Disease Classification Using Probability Distribution](https://mlsanity.com/paper/2406.01054/) (2024)
- [Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification](https://mlsanity.com/paper/2503.06056/) (2025)
- [Dual-Alignment Knowledge Retention for Continual Medical Image Segmentation](https://mlsanity.com/paper/2507.03638/) (2025)
- [Continual Multiple Instance Learning for Hematologic Disease Diagnosis](https://mlsanity.com/paper/2508.04368/) (2025)
- [Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning](https://mlsanity.com/paper/2508.10299/) (2025)
- [Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentation](https://mlsanity.com/paper/2509.23907/) (2025)
- [On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis](https://mlsanity.com/paper/2605.19201/) (2026)
- [Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning](https://mlsanity.com/paper/2609.32785/) (2026)

## Abstract (arXiv)

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-the-art 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 will be made publicly available.