Multi-scale Feature Enhancement in Multi-task Learning for Medical Image Analysis

• URL: http://arxiv.org/abs/2412.00351v1
• Date: Sat, 30 Nov 2024 04:20:05 GMT
• Title: Multi-scale Feature Enhancement in Multi-task Learning for Medical Image Analysis
• Authors: Phuoc-Nguyen Bui, Duc-Tai Le, Junghyun Bum, Hyunseung Choo,
• Abstract summary: Traditional deep learning methods in medical imaging often focus solely on segmentation or classification. We propose a simple yet effective UNet-based MTL model, where features extracted by the encoder are used to predict classification labels, while the decoder produces the segmentation mask. Experimental results across multiple medical datasets confirm the superior performance of our model in both segmentation and classification tasks.
• Score: 1.6916040234975798
• License:
• Abstract: Traditional deep learning methods in medical imaging often focus solely on segmentation or classification, limiting their ability to leverage shared information. Multi-task learning (MTL) addresses this by combining both tasks through shared representations but often struggles to balance local spatial features for segmentation and global semantic features for classification, leading to suboptimal performance. In this paper, we propose a simple yet effective UNet-based MTL model, where features extracted by the encoder are used to predict classification labels, while the decoder produces the segmentation mask. The model introduces an advanced encoder incorporating a novel ResFormer block that integrates local context from convolutional feature extraction with long-range dependencies modeled by the Transformer. This design captures broader contextual relationships and fine-grained details, improving classification and segmentation accuracy. To enhance classification performance, multi-scale features from different encoder levels are combined to leverage the hierarchical representation of the input image. For segmentation, the features passed to the decoder via skip connections are refined using a novel dilated feature enhancement (DFE) module, which captures information at different scales through three parallel convolution branches with varying dilation rates. This allows the decoder to detect lesions of varying sizes with greater accuracy. Experimental results across multiple medical datasets confirm the superior performance of our model in both segmentation and classification tasks, compared to state-of-the-art single-task and multi-task learning methods.

Related papers

• Investigating Self-Supervised Methods for Label-Efficient Learning [27.029542823306866]
  We study different self supervised pretext tasks, namely contrastive learning, clustering, and masked image modelling for their low-shot capabilities. We introduce a framework involving both mask image modelling and clustering as pretext tasks, which performs better across all low-shot downstream tasks. When testing the model on full scale datasets, we show performance gains in multi-class classification, multi-label classification and semantic segmentation.
  arXiv  Detail & Related papers  (2024-06-25T10:56:03Z)
• P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation [8.46409964236009]
  Diffusion models and multi-scale features are essential components in semantic segmentation tasks. We propose a new model for semantic segmentation known as the diffusion model with parallel multi-scale branches. Our model demonstrates superior performance based on the J1 metric on both the UAVid and Vaihingen Building datasets.
  arXiv  Detail & Related papers  (2024-05-30T19:40:08Z)
• Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation [79.05949524349005]
  We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from saliency maps. We also propose a cross-task affinity learning mechanism to learn pixel-level affinities from the saliency and segmentation feature maps.
  arXiv  Detail & Related papers  (2024-03-02T10:03:21Z)
• DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication [50.017055360261665]
  We introduce DiffVein, a unified diffusion model-based framework which simultaneously addresses vein segmentation and authentication tasks. For better feature interaction between these two branches, we introduce two specialized modules. In this way, our framework allows for a dynamic interplay between diffusion and segmentation embeddings.
  arXiv  Detail & Related papers  (2024-02-03T06:49:42Z)
• ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation [7.955518153976858]
  We propose an advanced 2D feature extraction method by combining the convolutional neural network and Transformer architectures. Our method is shown with better segmentation accuracy, especially on small organs.
  arXiv  Detail & Related papers  (2024-01-27T05:58:36Z)
• 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)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Part-guided Relational Transformers for Fine-grained Visual Recognition [59.20531172172135]
  We propose a framework to learn the discriminative part features and explore correlations with a feature transformation module. Our proposed approach does not rely on additional part branches and reaches state-the-of-art performance on 3-of-the-level object recognition.
  arXiv  Detail & Related papers  (2022-12-28T03:45:56Z)
• Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers [5.177947445379688]
  We propose a new segmentation model that combines convolutional neural networks with deep transformers. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-06-20T12:03:54Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)

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.