{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:04:44Z","timestamp":1773803084616,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"27","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Adapting computational pathology models to evolving clinical diagnostics via Class-Incremental Semantic Segmentation (CISS) is critical. However, this task imposes a unique CISS Trilemma: a simultaneous failure to preserve the intricate tissue background (stability), distinguish morphologically similar new nuclei (plasticity), and maintain a constant model size (scalability), all under a strict exemplar-free constraint. To resolve this, we introduce Palimpsest, a novel framework that systematically decouples these conflicting demands. Palimpsest integrates three synergistic mechanisms: a Parameter-Conserving Synthesis (PCS) module merges lightweight adapters to ensure scalability; a novel Similarity-Aware Centroid Recalibration (SCR) module executes differentiated recalibration to counteract non-uniform foreground drift, securing plasticity; and an Adaptive Residual Shading (ARS) module performs logit-space decoupling to preserve background integrity, ensuring stability. Extensive experiments on two histopathology datasets demonstrate that Palimpsest significantly outperforms state-of-the-art methods, achieving a superior stability-plasticity balance, particularly in challenging long-term incremental scenarios.<\/jats:p>","DOI":"10.1609\/aaai.v40i27.39458","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:30:37Z","timestamp":1773797437000},"page":"22940-22948","source":"Crossref","is-referenced-by-count":0,"title":["Palimpsest: Reconciling the CISS Trilemma for Incremental Nuclei Segmentation"],"prefix":"10.1609","volume":"40","author":[{"given":"Jiajia","family":"Li","sequence":"first","affiliation":[]},{"given":"Huisi","family":"Wu","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39458\/43419","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39458\/43419","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:30:38Z","timestamp":1773797438000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i27.39458","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}