AMN fuses Swin Transformer and ResNet-50 via adaptive gating and trains with focal, boundary, and uncertainty losses to reach 0.82 mean Dice on the seven-class CoNIC benchmark.
Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3
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abstract
As melanoma diagnoses increase across the US, automated efforts to identify malignant lesions become increasingly of interest to the research community. Segmentation of dermoscopic images is the first step in this process, thus accuracy is crucial. Although techniques utilizing convolutional neural networks have been used in the past for lesion segmentation, we present a solution employing the recently published DeepLab 3, an atrous convolution method for image segmentation. Although the results produced by this run are not ideal, with a mean Jaccard index of 0.498, we believe that with further adjustments and modifications to the compatibility with the DeepLab code and with training on more powerful processing units, this method may achieve better results in future trials.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation
AMN fuses Swin Transformer and ResNet-50 via adaptive gating and trains with focal, boundary, and uncertainty losses to reach 0.82 mean Dice on the seven-class CoNIC benchmark.