论文标题
显着皮肤病变通过扩张尺度特征融合网络进行分割
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
论文作者
论文摘要
皮肤镜图像中的皮肤病变检测对于通过计算机化设备对皮肤癌的准确和早期诊断至关重要。当前的皮肤病变细分方法在具有挑战性的环境中表现出较差的性能，例如不明显的病变边界，病变和周围区域之间的对比度较低，或者导致皮肤病变的分割或不足的异质背景。为了准确识别邻近区域的病变，我们提出了基于卷积分解的扩大规模融合网络。我们的网络旨在同时提取不同尺度的功能，这些功能在系统上融合以更好地检测。提出的模型具有令人满意的精度和效率。与最新模型进行了比较，进行了各种病变分割的实验。我们提出的模型始终展示最先进的结果。
Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches show poor performance in challenging circumstances such as indistinct lesion boundaries, low contrast between the lesion and the surrounding area, or heterogeneous background that causes over/under segmentation of the skin lesion. To accurately recognize the lesion from the neighboring regions, we propose a dilated scale-wise feature fusion network based on convolution factorization. Our network is designed to simultaneously extract features at different scales which are systematically fused for better detection. The proposed model has satisfactory accuracy and efficiency. Various experiments for lesion segmentation are performed along with comparisons with the state-of-the-art models. Our proposed model consistently showcases state-of-the-art results.
