YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging

• URL: http://arxiv.org/abs/2506.22955v1
• Date: Sat, 28 Jun 2025 17:21:25 GMT
• Title: YM-WML: A new Yolo-based segmentation Model with Weighted Multi-class Loss for medical imaging
• Authors: Haniyeh Nikkhah, Jafar Tanha, Mahdi Zarrin, SeyedEhsan Roshan, Amin Kazempour,
• Abstract summary: This study proposes YM-WML, a novel model for cardiac image segmentation.<n>The model integrates a robust backbone for effective feature extraction, a YOLOv11 neck for multi-scale feature aggregation, and an attention-based segmentation head.<n>On the ACDC dataset, YM-WML achieves a Dice Similarity Coefficient of 91.02, outperforming state-of-the-art methods.
• Score: 1.001970681951346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation poses significant challenges due to class imbalance and the complex structure of medical images. To address these challenges, this study proposes YM-WML, a novel model for cardiac image segmentation. The model integrates a robust backbone for effective feature extraction, a YOLOv11 neck for multi-scale feature aggregation, and an attention-based segmentation head for precise and accurate segmentation. To address class imbalance, we introduce the Weighted Multi-class Exponential (WME) loss function. On the ACDC dataset, YM-WML achieves a Dice Similarity Coefficient of 91.02, outperforming state-of-the-art methods. The model demonstrates stable training, accurate segmentation, and strong generalization, setting a new benchmark in cardiac segmentation tasks.

Related papers

• Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging [0.510750648708198]
  Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis.<n>Existing methods struggle with modality-specific biases and the need for extensive labeled datasets.<n>We propose a foundation model for whole-heart segmentation using a self-supervised learning framework based on a student-teacher architecture.
  arXiv  Detail & Related papers  (2025-03-24T14:47:54Z)
• Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation [53.723234136550055]
  We term the new learning paradigm as Partially Supervised Unpaired Multi-Modal Learning (PSUMML)<n>We propose a novel Decomposed partial class adaptation with snapshot Ensembled Self-Training (DEST) framework for it.<n>Our framework consists of a compact segmentation network with modality specific normalization layers for learning with partially labeled unpaired multi-modal data.
  arXiv  Detail & Related papers  (2025-03-07T07:22:42Z)
• Lagrange Duality and Compound Multi-Attention Transformer for Semi-Supervised Medical Image Segmentation [27.758157788769253]
  We propose a Lagrange Duality Consistency (LDC) Loss, integrated with Boundary-Aware Contrastive Loss, as the overall training objective for semi-supervised learning. We also introduce CMAformer, a novel network that synergizes the strengths of ResUNet and Transformer. Overall, our results indicate that CMAformer, combined with the feature fusion framework and the new consistency loss, demonstrates strong complementarity in semi-supervised learning ensembles.
  arXiv  Detail & Related papers  (2024-09-12T06:52:46Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation [0.0]
  We present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our study further showcases the potential of our model to flag low-quality segmentation from a given model.
  arXiv  Detail & Related papers  (2021-09-16T03:53:24Z)
• 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.