For the MIDOG mitosis detection challenge, we created a cascade algorithm consisting of a Mask-RCNN detector, followed by a classification ensemble consisting of ResNet50 and DenseNet201 to refine detected mitotic candidates. The MIDOG training data consists of 200 frames originating from four scanners, three of which are annotated for mitotic instances with centroid annotations. Our main algorithmic choices are as follows: first, to enhance the generalizability of our detector and classification networks, we use a state-of-the-art residual Cycle-GAN to transform each scanner domain to every other scanner domain. During training, we then randomly load, for each image, one of the four domains. In this way, our networks can learn from the fourth non-annotated scanner domain even if we don't have annotations for it. Second, for training the detector network, rather than using centroid-based fixed-size bounding boxes, we create mitosis-specific bounding boxes. We do this by manually annotating a small selection of mitoses, training a Mask-RCNN on this small dataset, and applying it to the rest of the data to obtain full annotations. We trained the follow-up classification ensemble using only the challenge-provided positive and hard-negative examples. On the preliminary test set, the algorithm scores an F1 score of 0.7578, putting us as the second-place team on the leaderboard.
翻译:为了应对MIDOG的线虫病检测挑战,我们创建了一个由Mask-RCNN探测器组成的连锁算法,该算法由Mask-RCNN探测器组成,然后由ResNet50和DenseNet201组成的分类组合组合,以完善检测到的线虫候选者。MIDOG培训数据由来自四台扫描仪的200个框架组成,其中三台是配有中子图解的线虫病例附加说明。我们的主要算法选择如下:首先,为了提高我们的探测器和分类网络的可普及性,我们使用一个最先进的剩余周期-GAN来将每个扫描域转换到其他扫描域。在培训期间,我们随机地为每个图像加载了四个域中的一个。这样,我们的网络就可以从第四个非附加说明的扫描仪域中学习200个框架,即使我们没有对它的说明。第二,用于对探测器网络进行培训,而不是使用基于丙型固定尺寸的捆绑框,我们创建了线虫-具体捆绑的框。我们这样做的方法是手写一个小型的缩标域域域域域域域域域域域域域域域域域域,在对每个图像中,训练一个马-RC-RC-RC-RCN-N-NLLLLAD