UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction

• URL: http://arxiv.org/abs/2509.11108v2
• Date: Thu, 02 Oct 2025 14:40:19 GMT
• Title: UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
• Authors: Zhi Chen, Le Zhang,
• Abstract summary: We introduce UltraUPConvNet, a universal framework for both ultrasound image classification and segmentation.<n>Our model achieves state-of-the-art performance on certain datasets with lower computational overhead.
• Score: 8.547397293290404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,700 annotations across seven different anatomical regions, our model achieves state-of-the-art performance on certain datasets with lower computational overhead. Our model weights and codes are available at https://github.com/yyxl123/UltraUPConvNet

Related papers

• ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology [0.6754906913334766]
  We propose a novel approach based on attention-driven feature fusion of convolutional neural networks (CNNs) and vision transformers (ViTs)<n>Our model achieved muIoU/muDice scores of 76.79%/86.87% on the GCPS dataset and 64.93%/76.60% on the PUMA dataset.
  arXiv  Detail & Related papers  (2025-10-23T17:21:06Z)
• A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications [77.3888788549565]
  We present EchoCare, a novel ultrasound foundation model for generalist clinical use.<n>We developed EchoCare via self-supervised learning on our curated, publicly available, large-scale dataset EchoCareData.<n>With minimal training, EchoCare outperforms state-of-the-art comparison models across 10 representative ultrasound benchmarks.
  arXiv  Detail & Related papers  (2025-09-15T10:05:31Z)
• EUIS-Net: A Convolutional Neural Network for Efficient Ultrasound Image Segmentation [12.84851020291695]
  EUIS-Net is a CNN network designed to segment ultrasound images efficiently and precisely. Four encoder-decoder blocks result in a notable decrease in computational complexity. The proposed EUIS-Net achieves mean IoU and dice scores of 78. 12%, 85. 42% and 84. 73%, 89. 01% in the BUSI and DDTI datasets, respectively.
  arXiv  Detail & Related papers  (2024-08-22T11:57:59Z)
• UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation [19.85119434049726]
  We propose UniUSNet, a universal framework for ultrasound image classification and segmentation. This model handles various ultrasound types, anatomical positions, and input formats, excelling in both segmentation and classification tasks. We plan to expand our dataset and refine the prompting mechanism, with model weights and code available at.
  arXiv  Detail & Related papers  (2024-06-03T09:49:54Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound [1.2903292694072621]
  Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture quality ultrasound images. We present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet) In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned.
  arXiv  Detail & Related papers  (2023-11-17T20:32:37Z)
• Learning from partially labeled data for multi-organ and tumor segmentation [102.55303521877933]
  We propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple datasets. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. We create a large-scale partially labeled Multi-Organ and Tumor benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors.
  arXiv  Detail & Related papers  (2022-11-13T13:03:09Z)
• Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs [55.78588835407174]
  Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
  arXiv  Detail & Related papers  (2022-08-17T09:07:45Z)
• Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks [48.732863591145964]
  We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
  arXiv  Detail & Related papers  (2021-10-12T21:22:24Z)
• KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations [59.65174244047216]
  We propose an over-complete architecture (Ki-Net) which involves projecting the data onto higher dimensions. This network, when augmented with U-Net, results in significant improvements in the case of segmenting small anatomical landmarks. We evaluate the proposed method on the task of brain anatomy segmentation from 2D Ultrasound of preterm neonates.
  arXiv  Detail & Related papers  (2020-06-08T18:59:24Z)
• Progressive Adversarial Semantic Segmentation [11.323677925193438]
  Deep convolutional neural networks can perform exceedingly well given full supervision. The success of such fully-supervised models for various image analysis tasks is limited to the availability of massive amounts of labeled data. We propose a novel end-to-end medical image segmentation model, namely Progressive Adrial Semantic (PASS)
  arXiv  Detail & Related papers  (2020-05-08T22:48:00Z)

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.