Precise medical segmentation of oral ulcers is mandatory and crucial for early diagnosis, but it remains a very challenging task due to rich backgrounds, overexposed or underexposed lesions, and the complex surrounding areas. Therefore, in order to address this challenge, this paper introduces HF-EdgeFormer, a novel hybrid model for oral ulcer segmentation on the AutoOral dataset. This U-shaped transformer-like architecture is, based on publicly available models, the second documented solution for oral ulcer segmentation and it explicitly integrates high-order frequency interactions by using multi-dimensional edge cues. At the encoding stage, a HFConv (High-order Focus Convolution) module divides the feature channels into local streams and global streams, performing learnable filtering via FFT and depth-wise convolutions. After that, it fuses them through stacks of focal transformers and attention gates. In addition to the HFConv block, there are two edge-aware units: the EdgeAware Localization module (that uses eight-direction Sobel filters) and a new Precision EdgeEnhance module (channel-wise Sobel fusion), both used in order to reinforce the boundaries. Skip connections imply Multi-dilated Attention Gates, accompanied by a Spacial-Channel Attention Bridge to accentuate lesion-consistent activations. Moreover, the novel architecture employs an innovative lightweight vision transformer-based bottleneck. It consists of four SegFormerBlock modules localized at the network’s deepest point, so we can achieve global relational modeling exactly where the largest receptive field is present. The model is trained on the AutoOral dataset (introduced by the same team that developed the HF-Unet arhitecture), but due to the limited available images, it needed to be extended by using extensive geometric and photometric augmentations (like RandomAffine, flips, and rotations). This novel architecture achieves a test Dice score of almost 82% and a little over 85% sensitivity while maintaining high precision and specificity, highly valuable in medical segmentation. These results surpass prior HF-UNet baselines while maintaining the model light, with minimal inference memory gains.
Cornoiu et al. (Thu,) studied this question.