Histopathology Image Normalization via Latent Manifold Compaction

• URL: http://arxiv.org/abs/2602.24251v1
• Date: Fri, 27 Feb 2026 18:26:59 GMT
• Title: Histopathology Image Normalization via Latent Manifold Compaction
• Authors: Xiaolong Zhang, Jianwei Zhang, Selim Sevim, Emek Demir, Ece Eksi, Xubo Song,
• Abstract summary: Batch effects arising from technical variations in histo staining protocols, scanners, and acquisition pipelines pose a persistent challenge for computational pathology.<n>We introduce Latent Manifold Compaction (LMC), an unsupervised representation learning framework that performs image harmonization by learning batch-invariant embeddings from a single source dataset.
• Score: 3.9126446725269894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge for computational pathology, hindering cross-batch generalization and limiting reliable deployment of models across clinical sites. In this work, we introduce Latent Manifold Compaction (LMC), an unsupervised representation learning framework that performs image harmonization by learning batch-invariant embeddings from a single source dataset through explicit compaction of stain-induced latent manifolds. This allows LMC to generalize to target domain data unseen during training. Evaluated on three challenging public and in-house benchmarks, LMC substantially reduces batch-induced separations across multiple datasets and consistently outperforms state-of-the-art normalization methods in downstream cross-batch classification and detection tasks, enabling superior generalization.

Related papers

• Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation [24.25178585285867]
  We introduce a Task Consistency Training (TCT) framework to address class imbalance without requiring extra models.<n>To avoid error propagation from low-consistency, potentially noisy data, we propose a filtering strategy to exclude such data.<n>Experiments on eight abdominal datasets from diverse clinical sites demonstrate our approach's effectiveness.
  arXiv  Detail & Related papers  (2025-09-05T01:04:32Z)
• Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection [53.137651284042434]
  Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples limits the effectiveness of existing methods.<n>We propose Generate grained Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework.<n>GAA generates realistic, diverse, and semantically aligned anomalies using only a small number of samples.
  arXiv  Detail & Related papers  (2025-07-13T12:56:59Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [49.11819337853632]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities [16.69453837626083]
  We propose a Correlation-decoupled Knowledge Distillation (CorrKD) framework for the Multimodal Sentiment Analysis (MSA) task under uncertain missing modalities. We present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics. We design a response-disentangled consistency distillation strategy to optimize the sentiment decision boundaries of the student network.
  arXiv  Detail & Related papers  (2024-04-25T09:35:09Z)
• Generalized Consistency Trajectory Models for Image Manipulation [59.576781858809355]
  Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration.<n>This work aims to unlock the full potential of consistency trajectory models (CTMs) by proposing generalized CTMs (GCTMs)<n>We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.
  arXiv  Detail & Related papers  (2024-03-19T07:24:54Z)
• ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization [42.810247034149214]
  Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard pipelines. This study highlights the importance and challenges of exploring Single Domain Generalization frameworks in the context of the classification task.
  arXiv  Detail & Related papers  (2024-03-14T13:50:44Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• The Whole Pathological Slide Classification via Weakly Supervised Learning [7.313528558452559]
  We introduce two pathological priors: nuclear disease of cells and spatial correlation of pathological tiles. We propose a data augmentation method that utilizes stain separation during extractor training. We then describe the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H&E-stained tissue images.
  arXiv  Detail & Related papers  (2023-07-12T16:14:23Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)

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.