OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging
Recent advances in representation learning often rely on holistic, black-box embeddings that entangle multiple semantic components, limiting interpretability and generalization. These issues are especially critical in medical imaging. To address these limitations, we propose an Organ-Wise Tokenization (OWT) framework with a Token Group-based Reconstruction (TGR) training paradigm. Unlike conventional approaches that produce holistic features, OWT explicitly disentangles an image into separable token groups, each corresponding to a distinct organ or semantic entity. Our design ensures each token group encapsulates organ-specific information, boosting interpretability, generalization, and efficiency while allowing fine-grained control in downstream tasks. Experiments on CT and MRI datasets demonstrate the effectiveness of OWT in not only achieving strong image reconstruction and segmentation performance, but also enabling novel semantic-level generation and retrieval applications that are out of reach for standard holistic embedding methods. These findings underscore the potential of OWT as a foundational framework for semantically disentangled representation learning, offering broad scalability and applicability to real-world medical imaging scenarios and beyond.
Code (1)
Tasks
Image ReconstructionRepresentation LearningSimilar Papers 제목 키워드 기반
On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts
While 3D foundational models have shown promise for promptable segmentation of medical volumes, their robustness to imprecise prompts remains under-explored. In this work, we aim to address this gap by systematically stu…
Medical Image SegmentationVaporetto: Efficient Japanese Tokenization Based on Improved Pointwise Linear Classification
This paper proposes an approach to improve the runtime efficiency of Japanese tokenization based on the pointwise linear classification (PLC) framework, which formulates the whole tokenization process as a sequence of li…
Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models
Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changing patient populations, and pathologies for which annotated training da…
Anomaly DetectionHow Important Is Tokenization in French Medical Masked Language Models?
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began wi…
Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization
Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech. Recent syllabic tokenization methods employ teacher-student distillation…
Boundary Detection