FaRMamba adds a multi-scale frequency transform module and a reconstruction auxiliary encoder to a Vision Mamba segmentation encoder, reporting improved Dice and mIoU on CAMUS, Mouse-cochlea, and Kvasir-Seg datasets.
Fast multi-scale edge-detection in medical ultrasound signals
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abstract
In this article we suggest a fast multi-scale edge-detection scheme for medical ultrasound signals. The edge-detector is based on well-known properties of the continuous wavelet trans- form. To achieve both good localization of edges and detect only significant edges, we study the maxima-lines of the wavelet transform. One can obtain the maxima-lines between two scales by computing the wavelet transform at several intermediate scales. To reduce computational effort and time we suggest a time-scale filtering procedure which uses only few scales to connect modulus-maxima across time-scale plane. The design of this procedure is based on a study of maxima-lines corresponding to edges typical for medical ultrasound signals. This study allows us to construct an algorithm for medical ultrasound signals which meets the demand for speed, but not on expense of reliability. The edge-detection algorithm has been applied to a large class of medical ultrasound sig- nals including tumour-, liver- and artery-images. Our results show that the proposed algorithm effectively detects major features in such signals, including edges with low contrast.
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FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation
FaRMamba adds a multi-scale frequency transform module and a reconstruction auxiliary encoder to a Vision Mamba segmentation encoder, reporting improved Dice and mIoU on CAMUS, Mouse-cochlea, and Kvasir-Seg datasets.