# An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging arXiv: https://arxiv.org/abs/2410.03359 ยท HTML: https://mlsanity.com/paper/2410.03359/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 110 references that are in the map. - First posted on arXiv 4 Oct 2024. ## Builds on (most cited first, 30 of 31) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015) - [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021) - [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016) - [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014) - [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018) - [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014) - [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018) - [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019) - [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021) - [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022) - [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019) - [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021) - [DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2202.00972/) (2022) - [How to Train Vision Transformer on Small-scale Datasets?](https://mlsanity.com/paper/2210.07240/) (2022) - [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (2020) - [ColorNet: Investigating the importance of color spaces for image classification](https://mlsanity.com/paper/1902.00267/) (2019) - [Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective](https://mlsanity.com/paper/2302.03751/) (2023) - [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022) - [Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation](https://mlsanity.com/paper/2010.03341/) (2020) - [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022) - [Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation](https://mlsanity.com/paper/1708.01928/) (2017) - [Switchable Normalization for Learning-to-Normalize Deep Representation](https://mlsanity.com/paper/1907.10473/) (2019) - [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021) - [Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation](https://mlsanity.com/paper/2111.01590/) (2021) - [Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2109.01408/) (2021) - [A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers](https://mlsanity.com/paper/2105.07763/) (2021) - [HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2209.07313/) (2022) - [ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization](https://mlsanity.com/paper/2209.12822/) (2022) ## Built on by (most cited first, 2 of 2) - [Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation](https://mlsanity.com/paper/2503.05214/) (2025) - [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026) ## Abstract (arXiv) Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.