TopSeg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation

• URL: http://arxiv.org/abs/2510.17346v1
• Date: Mon, 20 Oct 2025 09:43:39 GMT
• Title: TopSeg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation
• Abstract summary: TopSeg is a representation-centric framework that encodes PCG dynamics with multi-scale topological features.<n>We train exclusively on PhysioNet 2016 dataset with subject-level subsampling and perform external validation on CirCor dataset.<n>Results indicate that TopSeg provides a strong inductive bias for data-efficient, cross-dataset PCG segmentation.
• Score: 12.42019711058722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning approaches for heart-sound (PCG) segmentation built on time--frequency features can be accurate but often rely on large expert-labeled datasets, limiting robustness and deployment. We present TopSeg, a topological representation-centric framework that encodes PCG dynamics with multi-scale topological features and decodes them using a lightweight temporal convolutional network (TCN) with an order- and duration-constrained inference step. To evaluate data efficiency and generalization, we train exclusively on PhysioNet 2016 dataset with subject-level subsampling and perform external validation on CirCor dataset. Under matched-capacity decoders, the topological features consistently outperform spectrogram and envelope inputs, with the largest margins at low data budgets; as a full system, TopSeg surpasses representative end-to-end baselines trained on their native inputs under the same budgets while remaining competitive at full data. Ablations at 10% training confirm that all scales contribute and that combining H_0 and H_1 yields more reliable S1/S2 localization and boundary stability. These results indicate that topology-aware representations provide a strong inductive bias for data-efficient, cross-dataset PCG segmentation, supporting practical use when labeled data are limited.

Related papers

• CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification [52.452902154360565]
  CuBAS is an information-geometric framework for adaptive data selection in supervised classification.<n>A labeled dataset can be viewed as a statistical manifold, on which local curvature, estimated via the ratio of second to first-order observed Fisher information, faithfully encodes the geometric complexity of the data distribution.
  arXiv  Detail & Related papers  (2026-07-03T09:36:02Z)
• Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes [0.0]
  Derivative-controlled networks based on ChainzRule combine cubic layers with a lightweight forward-mode per-layer Jacobian penalty (DREG)<n>On the Pima Diabetes dataset, CR achieves strong low-data performance and maintains a consistent accuracy advantage over baselines from 5% to 100% training data.<n> Extensions to SST-5 show competitive or superior results in both frozen-embedding and BERT fine-tuned regimes.
  arXiv  Detail & Related papers  (2026-06-06T00:14:22Z)
• ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification [4.904219291349455]
  ECG signals require architectures that simultaneously capture fine-grained beat-level morphology and broader rhythm-level dependencies.<n>This paper proposes the Electrocardiogram Neighborhood Attention Transformer (ECG-NAT), a novel self-supervised learning approach tailored for ECG classification.<n>ECG-NAT achieves robust performance on benchmark datasets, with 88.1% accuracy using only 1% labeled data.
  arXiv  Detail & Related papers  (2026-05-13T08:50:00Z)
• Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data [73.20753799870282]
  We propose a novel Divide-and-Conquer Holistic Cognition Network (DHCNet)<n>DHCNet implements a divide-and-conquer strategy by decomposing holistic cues into spatially-associated subtle discrepancies.<n>DHCNet achieves remarkable performance on five widely-used Ultra-FGVC datasets.
  arXiv  Detail & Related papers  (2026-04-21T11:17:09Z)
• SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learning [0.7799711162530713]
  Current ML approaches either require impractical data aggregation or fail due to class imbalance.<n>We introduce SCOPE, a coreset framework for federated data that filters anomalies and adaptively prunes redundant data to long-tail skew.<n>SCOPE yields competitive global accuracy and robust convergence, all while achieving exceptional efficiency with a 128x to 512x reduction in uplink bandwidth.
  arXiv  Detail & Related papers  (2026-03-13T13:24:12Z)
• Phi-SegNet: Phase-Integrated Supervision for Medical Image Segmentation [1.76179873429447]
  We propose Phi-SegNet, a CNN-based architecture that incorporates phase-aware information at both architectural and optimization levels.<n>Phi-SegNet consistently achieved state-of-the-art performance on five public datasets spanning X-ray, US, histopathology, MRI, and colonoscopy.
  arXiv  Detail & Related papers  (2026-01-22T16:00:41Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• Graph-Based Fault Diagnosis for Rotating Machinery: Adaptive Segmentation and Structural Feature Integration [0.0]
  This paper proposes a graph-based framework for robust and interpretable multiclass fault diagnosis in rotating machinery.<n>It integrates entropy-optimized signal segmentation, time-frequency feature extraction, and graph-theoretic modeling to transform vibration signals into structured representations.<n>The proposed method achieves high diagnostic accuracy when evaluated on two benchmark datasets.
  arXiv  Detail & Related papers  (2025-04-29T13:34:52Z)
• DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification [11.194964108429252]
  DeepFRC is an end-to-end deep learning framework that jointly learns warping functions and a classification within a unified architecture.<n>We provide the first theoretical guarantees for such a joint model, proving its ability to approximate optimal warpings.<n>Experiments on synthetic and real-world datasets demonstrate that DeepFRC consistently outperforms state-of-the-art methods in both alignment quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-01-30T03:35:03Z)
• Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data [35.431340001608476]
  Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data.<n>This study introduces semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data.<n> Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification.
  arXiv  Detail & Related papers  (2024-11-27T18:59:50Z)
• Soft Random Sampling: A Theoretical and Empirical Analysis [59.719035355483875]
  Soft random sampling (SRS) is a simple yet effective approach for efficient deep neural networks when dealing with massive data. It selects a uniformly speed at random with replacement from each data set in each epoch. It is shown to be a powerful and competitive strategy with significant and competitive performance on real-world industrial scale.
  arXiv  Detail & Related papers  (2023-11-21T17:03:21Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference [86.03382625531951]
  DANCE is an automated simultaneous data-network co-optimization for efficient segmentation model training and inference.<n>It integrates automated data slimming which adaptively downsamples/drops input images and controls their corresponding contribution to the training loss guided by the images' spatial complexity.<n>Experiments and ablating studies demonstrate that DANCE can achieve "all-win" towards efficient segmentation.
  arXiv  Detail & Related papers  (2021-07-16T04:58:58Z)
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data [93.76907759950608]
  We propose a doubly kernel learning algorithm for vertically partitioned data. We show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels.
  arXiv  Detail & Related papers  (2020-08-14T05:46:56Z)
• Temporal Calibrated Regularization for Robust Noisy Label Learning [60.90967240168525]
  Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets. However, labeling large-scale data can be very costly and error-prone so that it is difficult to guarantee the annotation quality. We propose a Temporal Calibrated Regularization (TCR) in which we utilize the original labels and the predictions in the previous epoch together.
  arXiv  Detail & Related papers  (2020-07-01T04:48:49Z)

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.