MTC-Net: Multi-scale feature fusion network for medical image segmentation

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MTC-Net achieves superior accuracy in medical image segmentation across four public datasets, outperforming recent computer-aided diagnosis frameworks.
Benchmark on ISIC2018, BUSI, TN3K, and Kvasir-SEG evaluates deep separable convolution and self-attentive modules to preserve contextual continuity.
Highlights how multiscale feature fusion and cooperative aggregation resolve hazy lesion boundaries, overcoming limitations in standard convolutional neural networks.

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Benchmark evaluation demonstrates superior accuracy in multi-organ lesion segmentation, highlighting improved contextual feature extraction.

Ren et al. (2024) studied this question.

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Benchmark evaluation demonstrates superior accuracy in multi-organ lesion segmentation, highlighting improved contextual feature extraction.