Fueled by deep learning, computer-aided diagnosis achieves huge advances. However, out of controlled lab environments, algorithms could face multiple challenges. Open set recognition (OSR), as an important one, states that categories unseen in training could appear in testing. In medical fields, it could derive from incompletely collected training datasets and the constantly emerging new or rare diseases. OSR requires an algorithm to not only correctly classify known classes, but also recognize unknown classes and forward them to experts for further diagnosis. To tackle OSR, we assume that known classes could densely occupy small parts of the embedding space and the remaining sparse regions could be recognized as unknowns. Following it, we propose Open Margin Cosine Loss (OMCL) unifying two mechanisms. The former, called Margin Loss with Adaptive Scale (MLAS), introduces angular margin for reinforcing intra-class compactness and inter-class separability, together with an adaptive scaling factor to strengthen the generalization capacity. The latter, called Open-Space Suppression (OSS), opens the classifier by recognizing sparse embedding space as unknowns using proposed feature space descriptors. Besides, since medical OSR is still a nascent field, two publicly available benchmark datasets are proposed for comparison. Extensive ablation studies and feature visualization demonstrate the effectiveness of each design. Compared with state-of-the-art methods, MLAS achieves superior performances, measured by ACC, AUROC, and OSCR.

翻译：在深度学习的推动下，计算机辅助诊断取得了巨大进展。然而，在受控实验室环境之外，算法可能面临多重挑战。开放集识别（OSR）作为其中一个重要挑战，指出训练中未见过的类别可能在测试中出现。在医学领域，这可能源于不完整的训练数据集收集以及不断出现的新疾病或罕见疾病。OSR要求算法不仅能正确分类已知类别，还能识别未知类别并将其转交给专家进行进一步诊断。为解决OSR问题，我们假设已知类别能密集占据嵌入空间的一小部分，而剩余稀疏区域可被识别为未知。基于此，我们提出开放边际余弦损失（OMCL），它统一了两种机制。第一种称为自适应尺度边际损失（MLAS），通过引入角度边际来增强类内紧凑性和类间可分离性，并搭配自适应缩放因子以强化泛化能力。第二种称为开放空间抑制（OSS），通过使用所提特征空间描述符将稀疏嵌入空间识别为未知，从而打开分类器。此外，由于医学OSR仍是一个新兴领域，我们提出了两个公开基准数据集用于比较。大量消融研究和特征可视化证明了每种设计的有效性。与现有最优方法相比，MLAS在ACC、AUROC和OSCR指标上均取得了优越性能。