# BiasPruner: Debiased Continual Learning for Medical Image Classification

arXiv: https://arxiv.org/abs/2407.08609 · HTML: https://mlsanity.com/paper/2407.08609/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [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)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Supermasks in Superposition](https://mlsanity.com/paper/2006.14769/) (2020)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning](https://mlsanity.com/paper/2307.09218/) (2023)
- [Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing](https://mlsanity.com/paper/2207.10384/) (2022)
- [Continual Learning for Domain Adaptation in Chest X-ray Classification](https://mlsanity.com/paper/2001.05922/) (2020)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [When does Bias Transfer in Transfer Learning?](https://mlsanity.com/paper/2207.02842/) (2022)
- [FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning](https://mlsanity.com/paper/2208.10013/) (2022)
- [Continual Prune-and-Select: Class-incremental learning with specialized subnetworks](https://mlsanity.com/paper/2208.04952/) (2022)
- [Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification](https://mlsanity.com/paper/2203.09860/) (2022)
- [Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues](https://mlsanity.com/paper/2305.05807/) (2023)
- [Where is the Truth? The Risk of Getting Confounded in a Continual World](https://mlsanity.com/paper/2402.06434/) (2024)
- [Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts](https://mlsanity.com/paper/2208.03206/) (2022)
- [Sustaining Fairness via Incremental Learning](https://mlsanity.com/paper/2208.12212/) (2022)
- [Continual Learning in the Presence of Spurious Correlation](https://mlsanity.com/paper/2303.11863/) (2023)

## Built on by (most cited first, 12 of 12)

- [LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis](https://mlsanity.com/paper/2507.23001/) (2025)
- [Preventing Shortcut Learning in Medical Image Analysis through Intermediate Layer Knowledge Distillation from Specialist Teachers](https://mlsanity.com/paper/2511.17421/) (2025)
- [Subgroup Performance Analysis in Hidden Stratifications](https://mlsanity.com/paper/2503.10382/) (2025)
- [Subgroups Matter for Robust Bias Mitigation](https://mlsanity.com/paper/2505.21363/) (2025)
- [Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning](https://mlsanity.com/paper/2503.18403/) (2025)
- [Foundation Models as Class-Incremental Learners for Dermatological Image Classification](https://mlsanity.com/paper/2507.14050/) (2025)
- [Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models](https://mlsanity.com/paper/2512.13072/) (2025)
- [Debiasify: Self-Distillation for Unsupervised Bias Mitigation](https://mlsanity.com/paper/2411.00711/) (2024)
- [Conformal-in-the-Loop for Learning with Imbalanced Noisy Data](https://mlsanity.com/paper/2411.02281/) (2024)
- [Confounder-Free Continual Learning via Recursive Feature Normalization](https://mlsanity.com/paper/2507.09031/) (2025)
- [BM-CL: Bias Mitigation through the lens of Continual Learning](https://mlsanity.com/paper/2509.01730/) (2025)
- [CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation](https://mlsanity.com/paper/2604.25376/) (2026)

## Abstract (arXiv)

Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetting. Diverging from conventional perspectives on CL, our paper introduces a new perspective wherein forgetting could actually benefit the sequential learning paradigm. Specifically, we present BiasPruner, a CL framework that intentionally forgets spurious correlations in the training data that could lead to shortcut learning. Utilizing a new bias score that measures the contribution of each unit in the network to learning spurious features, BiasPruner prunes those units with the highest bias scores to form a debiased subnetwork preserved for a given task. As BiasPruner learns a new task, it constructs a new debiased subnetwork, potentially incorporating units from previous subnetworks, which improves adaptation and performance on the new task. During inference, BiasPruner employs a simple task-agnostic approach to select the best debiased subnetwork for predictions. We conduct experiments on three medical datasets for skin lesion classification and chest X-Ray classification and demonstrate that BiasPruner consistently outperforms SOTA CL methods in terms of classification performance and fairness. Our code is available here.