Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.

翻译：肺癌和新冠病毒肺炎在全球范围内具有最高的发病率和死亡率之一。对于医生而言，在疾病早期阶段识别病灶既困难又耗时。因此，多任务学习是一种从少量医疗数据中提取重要特征（如病灶）的有效方法，因为它能更好地学习泛化。我们提出了一种用于分类、分割、重建和检测的新型多任务框架。据我们所知，我们是首个将检测任务纳入多任务解决方案的研究团队。此外，我们还研究了在分割任务中使用两种不同主干网络和不同损失函数的可行性。