Understanding a surgical scene is crucial for computer-assisted surgery
systems to provide any intelligent assistance functionality. One way of
achieving this scene understanding is via scene segmentation, where every pixel
of a frame is classified and therefore identifies the visible structures and
tissues. Progress on fully segmenting surgical scenes has been made using
machine learning. However, such models require large amounts of annotated
training data, containing examples of all relevant object classes. Such fully
annotated datasets are hard to create, as every pixel in a frame needs to be
annotated by medical experts and, therefore, are rarely available. In this
work, we propose a method to combine multiple partially annotated datasets,
which provide complementary annotations, into one model, enabling better scene
segmentation and the use of multiple readily available datasets. Our method
aims to combine available data with complementary labels by leveraging mutual
exclusive properties to maximize information. Specifically, we propose to use
positive annotations of other classes as negative samples and to exclude
background pixels of binary annotations, as we cannot tell if they contain a
class not annotated but predicted by the model. We evaluate our method by
training a DeepLabV3 on the publicly available Dresden Surgical Anatomy
Dataset, which provides multiple subsets of binary segmented anatomical
structures. Our approach successfully combines 6 classes into one model,
increasing the overall Dice Score by 4.4% compared to an ensemble of models
trained on the classes individually. By including information on multiple
classes, we were able to reduce confusion between stomach and colon by 24%. Our
results demonstrate the feasibility of training a model on multiple datasets.
This paves the way for future work further alleviating the need for one large,
fully segmented datasets.

通过结合多个部分注释数据集，我们提出了一种方法将互补注释合并到一个模型中，实现更好的场景分割和使用多个可用数据集。我们的方法成功地将 6 个类别组合成一个模型，与单独训练在各个类别上的模型集合相比，总体 Dice 分数提高了 4.4%。通过包含多个类别的信息，我们成功将胃和结肠之间的混淆减少了 24%。结果证明了在多个数据集上进行模型训练的可行性，为未来进一步减少对一个大型完全分割数据集的需求铺平了道路。

一种综合数据集训练分割模型的综合模型

One model to use them all: Training a segmentation model with  complementary datasets

We present two techniques to improve landmark localization in images from
partially annotated datasets. Our primary goal is to leverage the common
situation where precise landmark locations are only provided for a small data
subset, but where class labels for classification or regression tasks related
to the landmarks are more abundantly available. First, we propose the framework
of sequential multitasking and explore it here through an architecture for
landmark localization where training with class labels acts as an auxiliary
signal to guide the landmark localization on unlabeled data. A key aspect of
our approach is that errors can be backpropagated through a complete landmark
localization model. Second, we propose and explore an unsupervised learning
technique for landmark localization based on having a model predict equivariant
landmarks with respect to transformations applied to the image. We show that
these techniques, improve landmark prediction considerably and can learn
effective detectors even when only a small fraction of the dataset has landmark
labels. We present results on two toy datasets and four real datasets, with
hands and faces, and report new state-of-the-art on two datasets in the wild,
e.g. with only 5\% of labeled images we outperform previous state-of-the-art
trained on the AFLW dataset.

本研究提出了两种技术来改善局部标记数据集中的地标定位。第一种技术是顺序多任务，利用分类或回归任务的类标签引导未标记数据的局部标记定位。第二种是基于无监督学习的方法，通过模型预测图片上的等变地标来提高地标定位，本文在两个玩具数据集和四个实际数据集上进行实验，包括手和脸部， 并在只有 5％标记图像的条件下，跑过了之前在 AFLW 数据集上的最新 state-of-the-art 算法。