Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures

• URL: http://arxiv.org/abs/2604.25817v1
• Date: Tue, 28 Apr 2026 16:26:14 GMT
• Title: Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures
• Abstract summary: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one scale often generalize poorly to another.<n>We evaluated this problem on the BreaKHis dataset using a strict patient-disjoint leave-one-magnification-out protocol.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to another. Here, we evaluated this problem on the BreaKHis dataset using a strict patient-disjoint leave-one-magnification-out protocol, comparing supervised baseline, baseline augmented with DCGAN-generated patches, and a gradient-reversal domain-general model designed to preserve discriminative information while suppressing magnification-specific variation. Across held-out magnifications, the domain-general model achieved the strongest overall discrimination and its clearest gain was observed when 200X was held out. By contrast, GAN augmentation produced inconsistent effects, improving some folds but degrading others, particularly at 400X. The domain-general model also yielded the lowest Brier score at 0.063 vs 0.089 at baseline. Sparse embedding analysis further revealed that domain-general training reduced average signature size more than three-fold (306 versus 1,074 dimensions) while preserving equivalent predictive performance (AUC: 0.967 vs 0.965; F1: 0.930 vs 0.931). It also increased cross-fold signature reproducibility from near-zero Jaccard overlap in the baseline to 0.99 between the 100X and 200X folds. These findings show that calibrated, compact, and transferable representations can be learned without added architectural complexity, with clear implications for the reliable deployment of computational pathology models across heterogeneous acquisition settings.

Related papers

• Text-Guided Diffusion-Based Adversarial Attacks on Chest X-Ray Images [1.167393095135318]
  We propose a text-guided diffusion-based adversarial framework that optimize learnable text conditioning while keeping the diffusion generator and target classifier frozen.<n>Our approach consistently produced the greatest degradation in classifier performance, reducing AUROC to 0.3885-0.5646 in binary classification and 0.4441-0.4878 in the multi-disease setting.<n>These findings reveal a clinically important discrepancy between human and machine interpretation and demonstrate the need to extend medical AI robustness evaluation beyond conventional pixel-space attacks.
  arXiv  Detail & Related papers  (2026-08-29T22:19:04Z)
• DNA: Dual-stage Native Attribution for Generated Image Source Tracing [75.65470221927096]
  Existing proactive methods rely on watermark embedding or model modification.<n>DNA is a coarse-to-fine framework that follows this hierarchy without additional neural-network training.<n>Experiments show that DNA achieves 89.11% end-to-end attribution accuracy.
  arXiv  Detail & Related papers  (2026-07-15T10:27:52Z)
• Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment [0.0]
  Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images.<n>We present a systematic slice-level robustness audit of SAM for spleen segmentation in abdominal CT using 1,051 nonempty slices from 41 volumes in the Medical Decathlon.
  arXiv  Detail & Related papers  (2026-04-28T14:16:20Z)
• Beyond Anatomy: Explainable ASD Classification from rs-fMRI via Functional Parcellation and Graph Attention Networks [6.923757075165361]
  Anatomical brain parcellations dominate rs-fMRI-based Autism Spectrum Disorder (ASD) classification.<n>We present a graph-based deep learning framework comparing anatomical (AAL) and functionally-derived (MSDL) parcellation strategies on the ABIDE I dataset.
  arXiv  Detail & Related papers  (2026-03-03T02:05:20Z)
• Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection [2.8547732086436306]
  A fundamental limitation of supervised deep learning is "Generalization Collapse"<n>We propose Latent Sculpting, a hierarchical two-stage representation learning framework.<n>We report an 88.89% detection rate on "Infiltration" scenarios.
  arXiv  Detail & Related papers  (2025-12-19T11:37:02Z)
• Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis [42.60508892284938]
  We present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging.<n>We tested 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts.<n>Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation.
  arXiv  Detail & Related papers  (2025-12-01T11:03:27Z)
• MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis [40.3028468133626]
  Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology.<n>textbfMeCaMIL, a causality-aware MIL framework, explicitly models demographic confounders through structured causal graphs.<n>MeCaMIL achieves superior fairness -- demographic disparity variance drops by over 65% relative reduction on average across attributes.
  arXiv  Detail & Related papers  (2025-11-14T06:47:21Z)
• MSRANetV2: An Explainable Deep Learning Architecture for Multi-class Classification of Colorectal Histopathological Images [3.4859776888706233]
  Colorectal cancer (CRC) is a leading worldwide cause of cancer-related mortality.<n>Deep learning algorithms have become a powerful approach in enhancing diagnostic precision and efficiency.<n>We propose a convolutional neural network architecture named MSRANetV2, specially optimized for the classification of colorectal tissue images.
  arXiv  Detail & Related papers  (2025-10-28T07:22:34Z)
• Robust Atypical Mitosis Classification with DenseNet121: Stain-Aware Augmentation and Hybrid Loss for Domain Generalization [16.048949963804123]
  We present a DenseNet-121-based framework tailored for atypical mitosis classification in the MIDOG 2025 (Track 2) setting.<n>Our method integrates stain-aware augmentation (Macenko), geometric and intensity transformations, and imbalance-aware learning via weighted sampling with a hybrid objective combining class-weighted binary cross-entropy and focal loss.<n> Trained end-to-end with AdamW and evaluated across multiple independent domains, the model demonstrates strong generalization under scanner and staining shifts, achieving balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on the official test set.
  arXiv  Detail & Related papers  (2025-10-26T11:24:55Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• Robust Representation via Dynamic Feature Aggregation [44.927408735490005]
  Deep convolutional neural network (CNN) based models are vulnerable to adversarial attacks. We propose a method, denoted as Dynamic Feature Aggregation, to compress the embedding space with a novel regularization. An averaging accuracy of 56.91% is achieved by our method on CIFAR-10 against various attack methods.
  arXiv  Detail & Related papers  (2022-05-16T06:22:15Z)
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization [45.00998416720726]
  We propose a framework to address the unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
  arXiv  Detail & Related papers  (2020-02-13T15:28:42Z)

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.