2024-01-23-rahman24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation	Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-attention (SA) mechanism. In this paper, we address this issue by introducing a Multi-scale hiERarchical vIsion Transformer (MERIT) backbone network, which improves the generalizability of the model by computing SA at multiple scales. We also incorporate an attention-based decoder, namely Cascaded Attention Decoding (CASCADE), for further refinement of multi-stage features generated by MERIT. Finally, we introduce an effective multi-stage feature mixing loss aggregation (MUTATION) method for better model training via implicit ensembling. Our experiments on two widely used medical image segmentation benchmarks (i.e., Synapse Multi-organ, ACDC) demonstrate the superior performance of MERIT over state-of-the-art methods. Our MERIT architecture and MUTATION loss aggregation can be used with downstream medical image and semantic segmentation tasks.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	rahman24a	0	Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation	1526	1544	1526-1544	1526	false	Rahman, Md Mostafijur and Marculescu, Radu	given family Md Mostafijur Rahman given family Radu Marculescu	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/rahman24a/rahman24a.pdf