IRS: Incremental Relationship-guided Segmentation for Digital Pathology

• URL: http://arxiv.org/abs/2505.22855v1
• Date: Wed, 28 May 2025 20:41:56 GMT
• Title: IRS: Incremental Relationship-guided Segmentation for Digital Pathology
• Abstract summary: Incremental Relationship-guided learning scheme to address temporally acquired, partially annotated data.<n>IRS effectively handles the multi-scale nature of pathological segmentation.<n>IRS is a robust approach for real-world digital pathology applications.
• Score: 13.534959964309895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practicality in diverse real-world applications. In healthcare, continual learning holds great promise for continuously acquired digital pathology data, which is collected in hospitals on a daily basis. However, panoramic segmentation on digital whole slide images (WSIs) presents significant challenges, as it is often infeasible to obtain comprehensive annotations for all potential objects, spanning from coarse structures (e.g., regions and unit objects) to fine structures (e.g., cells). This results in temporally and partially annotated data, posing a major challenge in developing a holistic segmentation framework. Moreover, an ideal segmentation model should incorporate new phenotypes, unseen diseases, and diverse populations, making this task even more complex. In this paper, we introduce a novel and unified Incremental Relationship-guided Segmentation (IRS) learning scheme to address temporally acquired, partially annotated data while maintaining out-of-distribution (OOD) continual learning capacity in digital pathology. The key innovation of IRS lies in its ability to realize a new spatial-temporal OOD continual learning paradigm by mathematically modeling anatomical relationships between existing and newly introduced classes through a simple incremental universal proposition matrix. Experimental results demonstrate that the IRS method effectively handles the multi-scale nature of pathological segmentation, enabling precise kidney segmentation across various structures (regions, units, and cells) as well as OOD disease lesions at multiple magnifications. This capability significantly enhances domain generalization, making IRS a robust approach for real-world digital pathology applications.

Related papers

• Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases [62.33465338932216]
  MV-BrainFM is a multi-view brain network foundation model designed to learn generalizable and scalable representations from brain networks constructed with arbitrary atlases.<n>Experiments on more than 20K subjects from 17 fMRI datasets show that MV-BrainFM consistently outperforms 14 existing brain network foundation models.
  arXiv  Detail & Related papers  (2026-03-20T11:55:00Z)
• Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models [45.285970665585914]
  We propose a comprehensive framework for Continual Learning.<n>We employ a multi-modal, multi-layer RAG system that provides real-time guidance for model fine-tuning.<n>We introduce a dynamic knowledge distillation framework.
  arXiv  Detail & Related papers  (2025-12-15T08:09:40Z)
• DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging [9.252889721110503]
  We introduce Dynamic Curriculum Learning for temporal Approximate (DaSE)<n>We leverage Rank Pool encoding to encode three-dimensional brain data into information-rich, two-dimensional dynamic representations.<n>We employ a curriculum learning strategy, guided by a Dynamic Group, to train the decoder.<n>DaSE consistently outperforms existing methods in accuracy, prediction, and interpretability.
  arXiv  Detail & Related papers  (2025-11-19T06:10:31Z)
• Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis [4.14360329494344]
  Adapting machine learning models to medical time series remains a challenge due to complex temporal dependencies and dynamic distribution shifts.<n>We propose a novel framework leveraging multi-view contrastive learning to integrate temporal patterns, derivative-based dynamics, and frequency-domain features.<n>Our method employs independent encoders and a hierarchical fusion mechanism to learn feature-invariant representations that are transferable across domains.
  arXiv  Detail & Related papers  (2025-06-27T17:06:16Z)
• A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT [67.34586036959793]
  There is no fully annotated CT dataset with all anatomies delineated for training.<n>We propose a novel continual learning-driven CT model that can segment complete anatomies.<n>Our single unified CT segmentation model, CL-Net, can highly accurately segment a clinically comprehensive set of 235 fine-grained whole-body anatomies.
  arXiv  Detail & Related papers  (2025-03-16T23:55:02Z)
• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification.<n>We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings.<n>A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact [0.34826922265324145]
  Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum.<n>The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images.<n>We explore the true potential of these innovations and their integration into clinical practice.
  arXiv  Detail & Related papers  (2025-02-12T11:57:11Z)
• L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
  Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations. We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
  arXiv  Detail & Related papers  (2024-06-07T12:01:37Z)
• Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI [11.314017805825685]
  We propose a divergence-aware dual-flow module with balanced rigidity and plasticity branches to decouple old and new tasks. We evaluate our framework on a brain tumor segmentation task with continually changing target domains.
  arXiv  Detail & Related papers  (2023-05-30T20:39:03Z)
• Critical Learning Periods for Multisensory Integration in Deep Networks [112.40005682521638]
  We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases of training. We show that critical periods arise from the complex and unstable early transient dynamics, which are decisive of final performance of the trained system and their learned representations.
  arXiv  Detail & Related papers  (2022-10-06T23:50:38Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)

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.