HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation

• URL: http://arxiv.org/abs/2209.07313v1
• Date: Thu, 15 Sep 2022 14:17:11 GMT
• Title: HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation
• Abstract summary: We present a neural network architecture for medical image segmentation of diabetic foot ulcers and colonoscopy polyps. HarDNet-DFUS delivers excellent performance for the colonoscopy polyp segmentation task.
• Score: 0.33727511459109766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a neural network architecture for medical image segmentation of diabetic foot ulcers and colonoscopy polyps. Diabetic foot ulcers are caused by neuropathic and vascular complications of diabetes mellitus. In order to provide a proper diagnosis and treatment, wound care professionals need to extract accurate morphological features from the foot wounds. Using computer-aided systems is a promising approach to extract related morphological features and segment the lesions. We propose a convolution neural network called HarDNet-DFUS by enhancing the backbone and replacing the decoder of HarDNet-MSEG, which was SOTA for colonoscopy polyp segmentation in 2021. For the MICCAI 2022 Diabetic Foot Ulcer Segmentation Challenge (DFUC2022), we train HarDNet-DFUS using the DFUC2022 dataset and increase its robustness by means of five-fold cross validation, Test Time Augmentation, etc. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean dice and was the first place winner. HarDNet-DFUS also deliver excellent performance for the colonoscopy polyp segmentation task. It achieves 0.924 mean Dice on the famous Kvasir dataset, an improvement of 1.2\% over the original HarDNet-MSEG. The codes are available on https://github.com/kytimmylai/DFUC2022 (for Diabetic Foot Ulcers Segmentation) and https://github.com/YuWenLo/HarDNet-DFUS (for Colonoscopy Polyp Segmentation).

Related papers

• An Improved Ensemble-Based Machine Learning Model with Feature Optimization for Early Diabetes Prediction [0.0]
  Diabetes is a serious worldwide health issue, and successful intervention depends on early detection.<n>To use extensive health survey data to create a machine learning framework for diabetes classification that is both accurate and comprehensible.<n>In our study, we proposed and developed a React Native-based application with a Python Flask backend to support early diabetes prediction.
  arXiv  Detail & Related papers  (2025-11-15T07:42:31Z)
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis [6.095029229301643]
  Experimental results on three datasets show significant improvements in overall accuracy, sensitivity, and specificity compared to traditional methods. This underscores the potential of deep learning models for robust diabetes diagnosis.
  arXiv  Detail & Related papers  (2024-03-12T10:18:59Z)
• FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation [3.880691536038042]
  FUSegNet is a new model for foot ulcer segmentation in diabetes patients. It uses the pre-trained EfficientNet-b7 as a backbone to address the issue of limited training samples.
  arXiv  Detail & Related papers  (2023-05-04T16:07:22Z)
• Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation [3.9903913600002507]
  In this paper, we propose a new diabetic foot ulcers dataset, namely DFUC2022. We assess whether the clinical delineations are machine interpretable by deep learning networks or if image processing refined contour should be used. This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results.
  arXiv  Detail & Related papers  (2022-04-22T09:54:35Z)
• Self-Supervised U-Net for Segmenting Flat and Sessile Polyps [63.62764375279861]
  Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos.
  arXiv  Detail & Related papers  (2021-10-17T09:31:20Z)
• Automatic Foot Ulcer segmentation Using an Ensemble of Convolutional Neural Networks [3.037637906402173]
  We propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and UNet, to perform foot ulcer segmentation. Our method achieved state-of-the-art data-based Dice scores of 92.07% and 88.80%, respectively.
  arXiv  Detail & Related papers  (2021-09-03T09:55:04Z)
• High performing ensemble of convolutional neural networks for insect pest image detection [124.23179560022761]
  Pest infestation is a major cause of crop damage and lost revenues worldwide. We generate ensembles of CNNs based on different topologies. Two new Adam algorithms for deep network optimization are proposed.
  arXiv  Detail & Related papers  (2021-08-28T00:49:11Z)
• DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation [0.3734402152170273]
  We propose a novel architecture called DDANet'' based on a dual decoder attention network. Experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577.
  arXiv  Detail & Related papers  (2020-12-30T17:52:35Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.