Patch Network for medical image Segmentation
This work addresses the need for efficient and accurate segmentation tools in medical imaging, though it appears incremental by combining existing methods.
The paper tackles the challenge of balancing speed and accuracy in medical image segmentation by introducing a Patch Network (PNet) that integrates Swin Transformer concepts into a convolutional neural network, achieving state-of-the-art performance in both speed and accuracy on polyp and skin lesion segmentation datasets.
Accurate and fast segmentation of medical images is clinically essential, yet current research methods include convolutional neural networks with fast inference speed but difficulty in learning image contextual features, and transformer with good performance but high hardware requirements. In this paper, we present a Patch Network (PNet) that incorporates the Swin Transformer notion into a convolutional neural network, allowing it to gather richer contextual information while achieving the balance of speed and accuracy. We test our PNet on Polyp(CVC-ClinicDB and ETIS- LaribPolypDB), Skin(ISIC-2018 Skin lesion segmentation challenge dataset) segmentation datasets. Our PNet achieves SOTA performance in both speed and accuracy.