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arxiv: 2605.17729 · v1 · pith:NZIOW4YBnew · submitted 2026-05-18 · 💻 cs.CV · cs.AI· cs.LG

Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis

Danu Kim This is my paper

Pith reviewed 2026-05-20 12:58 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords domain incremental learningcontinual learningchest X-raypneumonia detectionreplay methodclass-aware lossdomain shift
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The pith

A replay-based continual learning method with class-aware replay and loss adapts chest X-ray pneumonia detection to new domains without forgetting prior ones.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper proposes a replay-based domain-incremental continual learning approach to let chest X-ray models handle variations in imaging devices and protocols across clinical settings. It adds class-aware balanced replay to keep past domain examples balanced in limited memory and a class-aware loss to reweight imbalances during updates. The goal is to avoid catastrophic forgetting so models stay accurate on pneumonia detection as new data arrives over time. Tests on a modified PneumoniaMNIST dataset with five simulated domains yield 88.66 percent average accuracy, exceeding experience replay, fine-tuning, and joint training. This setup targets resilient performance in changing real-world conditions like those during a pandemic.

Core claim

The paper claims that a replay-based domain-incremental continual learning framework, when equipped with class-aware balanced replay to preserve balanced class representations in constrained memory and a class-aware loss to dynamically reweight imbalances, enables models to adapt to cross-domain variations in chest X-rays while preventing catastrophic forgetting, as shown by 88.66 percent average accuracy on a five-domain shifted PneumoniaMNIST dataset that outperforms Experience Replay, Fine-Tuning, and Joint Training baselines.

What carries the argument

Class-aware balanced replay paired with class-aware loss inside a replay-based domain-incremental continual learning setup, which stores and replays balanced past-domain examples while adjusting training weights for class balance.

If this is right

  • Models can receive sequential updates from new clinical domains while retaining detection accuracy on earlier domains.
  • Class representation stays balanced in the replay buffer, reducing bias toward majority classes during incremental training.
  • Full retraining from scratch becomes unnecessary when fresh data from varied imaging sources appears.
  • Detection consistency holds across shifts in acquisition protocols without requiring joint access to all prior data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same replay structure could apply to other medical imaging tasks such as tumor detection in CT scans where domain shifts occur.
  • If real clinical variations exceed the simulated ones, the accuracy gain might shrink and require larger memory buffers.
  • Pairing this approach with federated learning could let multiple institutions contribute domain data without centralizing raw images.

Load-bearing premise

The five simulated domains in the modified PneumoniaMNIST dataset capture the real-world differences in imaging devices, acquisition protocols, and institutional conditions seen in clinical practice.

What would settle it

Running the method on chest X-ray collections from multiple real hospitals with distinct equipment and observing whether average accuracy falls below 88.66 percent or loses its edge over the experience replay and fine-tuning baselines.

Figures

Figures reproduced from arXiv: 2605.17729 by Danu Kim.

Figure 1
Figure 1. Figure 1: System diagram of the proposed method. such as Experience Replay (ER) [6], are effective across these settings. Within this context, domain-incremental learning is vital for medical imaging, where input distributions shift across devices or clinical environments. This study introduces a domain-incremental learning method integrating class-aware balanced replay and class-aware loss reweighting to mitigate c… view at source ↗
Figure 2
Figure 2. Figure 2: Performance comparison of the proposed method with [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy of the proposed method for buffer sizes. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains limited due to variations in imaging devices, acquisition protocols, and institutional conditions. This study introduces a replay-based domain-incremental continual learning designed to enable continual adaptation to cross-domain variations without catastrophic forgetting. The proposed method incorporates a class-aware balanced replay to maintain balanced class representation within a constrained memory and a class-aware loss to dynamically reweight class imbalance during training. Experiments conducted on a domain-shifted PneumoniaMNIST dataset consisting of five simulated domains demonstrate that the proposed method achieves an average accuracy of 88.66%, outperforming Experience Replay, Fine-Tuning, and Joint Training baselines. These findings highlight the efficacy of the proposed approach in achieving robust and consistent pneumonia detection across clinical environment variations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces a replay-based domain-incremental continual learning method for pneumonia detection in chest X-rays. It incorporates class-aware balanced replay to maintain class balance in memory and a class-aware loss to handle imbalance, aiming to adapt across domains without catastrophic forgetting. On a modified PneumoniaMNIST dataset with five simulated domains, the method reports an average accuracy of 88.66%, outperforming Experience Replay, Fine-Tuning, and Joint Training baselines.

Significance. If the empirical gains are confirmed with rigorous statistics and more realistic data, the work could advance continual learning techniques for medical imaging under distribution shifts, a relevant challenge for pandemic scenarios involving new imaging sources. The focus on constrained-memory replay with class awareness offers a practical direction, though the simulated evaluation constrains immediate clinical implications.

major comments (2)
  1. [Experiments section / Table reporting accuracies] The experimental results, including the reported 88.66% average accuracy, are presented without error bars, standard deviations across multiple runs, or statistical significance tests against the baselines (Experience Replay, Fine-Tuning, Joint Training). This weakens the central empirical claim of consistent outperformance.
  2. [Dataset / Experimental setup] The construction of the five simulated domains from PneumoniaMNIST lacks sufficient detail on the specific transformations, noise models, or protocol variations used. Without this, it is difficult to assess whether the shifts adequately proxy real clinical variations in devices, acquisition protocols, and institutions that the introduction and title frame as the target setting.
minor comments (1)
  1. [Abstract and Introduction] The abstract and introduction could more explicitly link the class-aware components to the pandemic-resilience motivation to strengthen the narrative flow.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which have helped us improve the manuscript. We provide point-by-point responses to the major comments below and have revised the paper to address the concerns raised.

read point-by-point responses

  1. Referee: The experimental results, including the reported 88.66% average accuracy, are presented without error bars, standard deviations across multiple runs, or statistical significance tests against the baselines (Experience Replay, Fine-Tuning, Joint Training). This weakens the central empirical claim of consistent outperformance.

    Authors: We agree that the lack of error bars, standard deviations, and statistical tests weakens the empirical claims. In the revised manuscript, we have rerun all experiments across five independent trials with different random seeds and now report mean accuracy with standard deviation for each method. We have also added paired statistical significance tests against the baselines in the updated results table and Experiments section. revision: yes

  2. Referee: The construction of the five simulated domains from PneumoniaMNIST lacks sufficient detail on the specific transformations, noise models, or protocol variations used. Without this, it is difficult to assess whether the shifts adequately proxy real clinical variations in devices, acquisition protocols, and institutions that the introduction and title frame as the target setting.

    Authors: We acknowledge that the original description of the domain construction was insufficiently detailed. In the revised manuscript, we have substantially expanded the Dataset and Experimental Setup section to provide a full account of the transformations, noise models, and parameter settings used to generate each of the five simulated domains from PneumoniaMNIST. This added information clarifies the intended proxy for real-world clinical variations. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical performance claims

full rationale

The paper reports an empirical result: a proposed replay-based continual learning method with class-aware balanced replay and class-aware loss achieves 88.66% average accuracy on a five-domain simulated PneumoniaMNIST dataset and outperforms Experience Replay, Fine-Tuning, and Joint Training. No equations, derivations, or fitted parameters are presented as predictions. The central claim rests on standard experimental comparisons rather than any self-definitional reduction, self-citation load-bearing step, or ansatz smuggled via prior work. The simulation of domains is an explicit modeling choice whose fidelity is an external validity question, not a circularity issue internal to the reported numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard continual learning assumptions about replay preventing forgetting and on the representativeness of simulated domain shifts; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Replay of stored examples from prior domains prevents catastrophic forgetting during incremental training on new domains.
    Core premise of replay-based continual learning invoked to justify the method.
  • domain assumption Simulated domain shifts in PneumoniaMNIST approximate real clinical variations in imaging conditions.
    Assumption required to generalize experimental results to practical settings.

pith-pipeline@v0.9.0 · 5659 in / 1378 out tokens · 49847 ms · 2026-05-20T12:58:56.675942+00:00 · methodology

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Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages · 2 internal anchors

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