Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis

Salvato in:

Dettagli Bibliografici
Pubblicato in:	arXiv.org (Dec 19, 2024), p. n/a
Autore principale:	Tang, Kunming
Altri autori:	Jiang, Zhiguo, Shi, Jun, Wang, Wei, Wu, Haibo, Zheng, Yushan
Pubblicazione:	Cornell University Library, arXiv.org
Soggetti:	Data analysis Data augmentation Image analysis Machine learning Representations Stability augmentation
Accesso online:	Citation/Abstract Full text outside of ProQuest
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Descrizione
Abstract:	Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods.
ISSN:	2331-8422
Fonte:	Engineering Database