Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2002.08694v1
• Date: Thu, 20 Feb 2020 12:00:24 GMT
• Title: Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
• Authors: Xiaohong Wang, Xudong Jiang, Henghui Ding, and Jun Liu
• Abstract summary: We propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. We also propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers.
• Score: 28.300486641368234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.

Related papers

• Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer [4.672688418357066]
  We propose a novel Transformer Diffusion (DTS) model for robust segmentation in the presence of noise. Our model, which analyzes the morphological representation of images, shows better results than the previous models in various medical imaging modalities.
  arXiv  Detail & Related papers  (2024-08-01T07:35:54Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• 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)
• FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations [4.022446255159328]
  Interpretable deep learning models have received widespread attention in the field of image recognition. Many interpretability models that have been proposed still have problems of insufficient accuracy and interpretability in medical image disease diagnosis. We propose feature-driven inference network (FeaInfNet) to solve these problems.
  arXiv  Detail & Related papers  (2023-12-04T13:09:00Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs [59.19218582436495]
  We propose an unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. We characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales.
  arXiv  Detail & Related papers  (2023-09-05T02:15:51Z)
• LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion [4.819821513256158]
  LCAUnet is proposed to improve the ability of complementary representation with fusion of edge and body features. Experiments on public available dataset ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-01T14:05:53Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network [28.709314434820953]
  Current skin lesion segmentation approaches show poor performance in challenging circumstances. We propose a dilated scale-wise feature fusion network based on convolution factorization. Our proposed model consistently showcases state-of-the-art results.
  arXiv  Detail & Related papers  (2022-05-20T16:08:37Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)

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.