Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study

Table 4 Model selection for this comparative study

Model	Model name	Encoder size	Applications
CNN-based	U-Net[36]	NA	General segmentation, Biomedical segmentation
	ResNet[37] + U-Net	ResNet501	General segmentation, Biomedical segmentation
	ConvNeXt[38] + UPerNet[39]	ConvNeXt-T1	General segmentation
	M2SNet[40]	Res2Net50-v1b-26w-4s1[41]	Medical segmentation
	Dilated SegNet[42]	ResNet501	Polyp segmentation
	PraNet[27]	Res2Net50-v1b-26w-4s1	Polyp segmentation
Transformer-based	SwinV2[43] + UPerNet	SwinV2-T1	General segmentation
	SETR-MLA[44]	ViT-B-161[22]	General segmentation
	SegFormer[45]	MiT-B21	General segmentation
	TransUNet[46]	ResNet501 and ViT-B-161	Medical segmentation
	PVTV2[47] + EMCAD[48]	PVTV2-B21	Medical segmentation
	FCBFormer[26]	PVTV2-B21	Polyp segmentation
Mamba-based	Swin-UMamba[49]	VSSM-encoder1	Medical segmentation
	Swin-UMamba-D[49]	VSSM-encoder1	Medical segmentation
	U-Mamba-Bot[50]	NA	Biomedical segmentation
	U-Mamba-Enc[50]	NA	Biomedical segmentation
	VM-UNETV2[51]	VM-UNET-encoder1	Medical segmentation

¹Indicates pre-trained with ImageNet-1K dataset.

CNN: Convolutional neural network; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; EMCAD: Efficient multi-scale convolutional decoding; VSSM: Visual-state-space-model; NA: Not applicable.