Melanoma is a serious form of skin cancer with high mortality rate at later stages. Fortunately, when detected early, the prognosis of melanoma is promising and malignant melanoma incidence rates are relatively low. As a result, datasets are heavily imbalanced which complicates training current state-of-the-art supervised classification AI models. We propose to use generative models to learn the benign data distribution and detect Out-of-Distribution (OOD) malignant images through density estimation. Normalizing Flows (NFs) are ideal candidates for OOD detection due to their ability to compute exact likelihoods. Nevertheless, their inductive biases towards apparent graphical features rather than semantic context hamper accurate OOD detection. In this work, we aim at using these biases with domain-level knowledge of melanoma, to improve likelihood-based OOD detection of malignant images. Our encouraging results demonstrate potential for OOD detection of melanoma using NFs. We achieve a 9\% increase in Area Under Curve of the Receiver Operating Characteristics by using wavelet-based NFs. This model requires significantly less parameters for inference making it more applicable on edge devices. The proposed methodology can aid medical experts with diagnosis of skin-cancer patients and continuously increase survival rates. Furthermore, this research paves the way for other areas in oncology with similar data imbalance issues\footnote{Code available at: https://github.com/A-Vzer/WaveletFlowPytorch}
翻译:幸运的是,如果早期发现黑皮瘤的预测前景良好,恶性黑皮瘤发病率相对较低。结果,数据集严重失衡,使目前最先进的受监督的AI类分类模型培训复杂化。我们提议使用基因化模型来学习良性数据分布,并通过密度估计探测出传播的恶性图象。正常化流动(NFs)是OOD检测的理想对象,因为他们有能力进行精确概率的计算。然而,它们偏向明显的图形特征而不是语义背景的诱导偏向妨碍了对OOD的准确检测。在这项工作中,我们的目标是利用这些对黑皮瘤的域级知识来利用这些偏差来改进对恶性图像的基于OOOD的检测。我们令人鼓舞的结果显示OOD有可能利用NF对黑皮瘤进行检测。我们在Curve地区通过使用基于波状的NFPs,使接收者操作性特征增加了9 ⁇ 。这个模型需要使用更精确的深度的深度参数,从而可以更精确地评估这些病床的皮肤诊断方法。