Footprint-Guided Exemplar-Free Continual Histopathology Report Generation

• URL: http://arxiv.org/abs/2602.23817v1
• Date: Fri, 27 Feb 2026 08:58:03 GMT
• Title: Footprint-Guided Exemplar-Free Continual Histopathology Report Generation
• Authors: Pratibha Kumari, Daniel Reisenbüchler, Afshin Bozorgpour, yousef Sadegheih, Priyankar Choudhary, Dorit Merhof,
• Abstract summary: We introduce an exemplar-free continual learning framework for WSI-to-report generation.<n>The core idea is a compact domain footprint built in a frozen patch-embedding space.<n>Our approach outperforms exemplar-free and limited-buffer rehearsal baselines.
• Score: 3.361593315894868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid progress in vision-language modeling has enabled pathology report generation from gigapixel whole-slide images, but most approaches assume static training with simultaneous access to all data. In clinical deployment, however, new organs, institutions, and reporting conventions emerge over time, and sequential fine-tuning can cause catastrophic forgetting. We introduce an exemplar-free continual learning framework for WSI-to-report generation that avoids storing raw slides or patch exemplars. The core idea is a compact domain footprint built in a frozen patch-embedding space: a small codebook of representative morphology tokens together with slide-level co-occurrence summaries and lightweight patch-count priors. These footprints support generative replay by synthesizing pseudo-WSI representations that reflect domain-specific morphological mixtures, while a teacher snapshot provides pseudo-reports to supervise the updated model without retaining past data. To address shifting reporting conventions, we distill domain-specific linguistic characteristics into a compact style descriptor and use it to steer generation. At inference, the model identifies the most compatible descriptor directly from the slide signal, enabling domain-agnostic setup without requiring explicit domain identifiers. Evaluated across multiple public continual learning benchmarks, our approach outperforms exemplar-free and limited-buffer rehearsal baselines, highlighting footprint-based generative replay as a practical solution for deployment in evolving clinical settings.

Related papers

• SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes [19.000998531934865]
  We introduce a framework that synthesizes compact task-specific parameterizations from a learned dictionary of prototypes conditioned on lightweight task descriptors.<n>The architecture preserves interpretability through motif-aware probes and a motif discovery pipeline.
  arXiv  Detail & Related papers  (2026-02-01T06:30:31Z)
• DynaPURLS: Dynamic Refinement of Part-aware Representations for Skeleton-based Zero-Shot Action Recognition [51.80782323686666]
  We introduce textbfDynaPURLS, a unified framework that establishes robust, multi-scale visual-semantic correspondences.<n>Our framework leverages a large language model to generate hierarchical textual descriptions that encompass both global movements and local body-part dynamics.<n>Experiments on three large-scale benchmark datasets, including NTU RGB+D 60/120 and PKU-MMD, demonstrate that DynaPURLS significantly outperforms prior art.
  arXiv  Detail & Related papers  (2025-12-12T10:39:10Z)
• A Prototype-Guided Coarse Annotations Refining Approach for Whole Slide Images [4.562061988943637]
  Fine-grained annotations in whole slide images show the boundaries of various pathological regions.<n>Existing methods for refining coarse annotations often rely on extensive training samples or clean datasets.<n>In this paper, we propose a prototype-guided approach.
  arXiv  Detail & Related papers  (2025-03-25T07:34:06Z)
• Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition [57.97930719585095]
  We introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets. The results showcase the universality and superior performance of PURLS, surpassing prior skeleton-based solutions and standard baselines from other domains.
  arXiv  Detail & Related papers  (2024-06-19T08:22:32Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Memory-Efficient Prompt Tuning for Incremental Histopathology Classification [69.46798702300042]
  We present a memory-efficient prompt tuning framework to cultivate model generalization potential in economical memory cost. We have extensively evaluated our framework with two histopathology tasks, i.e., breast cancer metastasis classification and epithelium-stroma tissue classification.
  arXiv  Detail & Related papers  (2024-01-22T03:24:45Z)
• PRIOR: Prototype Representation Joint Learning from Medical Images and Reports [19.336988866061294]
  We present a prototype representation learning framework incorporating both global and local alignment between medical images and reports. In contrast to standard global multi-modality alignment methods, we employ a local alignment module for fine-grained representation. A sentence-wise prototype memory bank is constructed, enabling the network to focus on low-level localized visual and high-level clinical linguistic features.
  arXiv  Detail & Related papers  (2023-07-24T07:49:01Z)
• Unsupervised Continual Semantic Adaptation through Neural Rendering [32.099350613956716]
  We study continual multi-scene adaptation for the task of semantic segmentation. We propose training a Semantic-NeRF network for each scene by fusing the predictions of a segmentation model. We evaluate our approach on ScanNet, where we outperform both a voxel-based baseline and a state-of-the-art unsupervised domain adaptation method.
  arXiv  Detail & Related papers  (2022-11-25T09:31:41Z)
• Semi-Supervised Domain Adaptation with Prototypical Alignment and Consistency Learning [86.6929930921905]
  This paper studies how much it can help address domain shifts if we further have a few target samples labeled. To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks. Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
  arXiv  Detail & Related papers  (2021-04-19T08:46:08Z)
• Transferring and Regularizing Prediction for Semantic Segmentation [115.88957139226966]
  In this paper, we exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. We present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties as constraints to regularize model transfer in an unsupervised fashion. Extensive experiments are conducted to verify the proposal of RPT on the transfer of models trained on GTA5 and SYNTHIA (synthetic data) to Cityscapes dataset (urban street scenes)
  arXiv  Detail & Related papers  (2020-06-11T16:19:41Z)

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.