  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).
