###### Abstract  
The key to training deep learning (DL) segmentation models lies in the collection of annotated data. The annotation process is, however, generally expensive in human resources. Our paper leverages deep or traditional machine learning methods trained on a small set of manually labeled data to automatically generate pseudo-labels on large datasets, which are then used to train so-called data-reinforced deep learning models. The relevance of the approach is demonstrated in two applicative scenarios that are distinct both in terms of task and pseudo-label generation procedures, enlarging the scope of the outcomes of our study. Our experiments reveal that (i) data reinforcement helps, even with error-prone pseudo-labels, (ii) convolutional neural networks have the capability to regularize their training with respect to labeling errors, and (iii) there is an advantage to increasing diversity when generating the pseudo-labels, either by enriching the manual annotation through accurate annotation of singular samples, or by considering soft pseudo-labels per sample when prior information is available about their certainty. traditional machine learning; segmentation; pseudo-label +
Footnote †: journal: Medical  
The envisioned semi-supervised framework is depicted in Figure 1. A small set of (semi-)manually annotated samples are considered to train an initial and relatively weak segmentation model. This initial model is then used to annotate a potentially large set of unlabeled data, resulting in error-prone labels, which are used to supervise the training of a convolutional neural network. This neural network is denoted CNN* in the following, and is named the _data-reinforced_ model because it leverages the distribution of patterns in the unlabeled dataset to diffuse the strictly manually supervised model. In a sense, the unlabeled data and associated pseudo-labels are used to distill the knowledge captured by the supervised model. Two flavors of the pseudo-label generation framework are presented in Figure 2a,b. They differ in the way they exploit traditional machine learning approaches to collect manual annotations and turn them into pseudo-labels. They have been designed to address the specificities of the segmentation problem they are dealing with. Figure 2a considers a scenario where the objects to segment are easy to delineate manually. Hence, manual labels are assumed to be available for a few images and are used to supervise the training of a conventional semi-naive Bayesian estimation model. This model provides class probability estimates on unlabeled data, which are then used to define pseudo-labels and supervise the training of the data-reinforced CNN* model. Our experiments in Section 4 demonstrate that adopting a stochastic approach to randomly turn the soft probabilities into pseudo-labels at each training iteration is advantageous since it limits the potential bias induced by systematic errors of the Bayesian estimator. In contrast, Figure 2b considers a case where objects have complex shapes, thereby requiring some semi-automatic approach to be delineated. In our study, various machine learning models (random forests, gradient-boosted detection trees, and even CNNs) have been considered to assign labels a few initial images. In practice, those machine learning models are trained interactively bymanually assigning labels to image parts (strokes or blocks). This is performed either on individual images (one model per image) or jointly on the whole set of initial images (one joint model for all images). When one model is trained per image, a CNN is then trained from the whole set of labeled images to aggregate the entire annotation knowledge in a single joint model. The gradient boosted trees (GBT) model is quite attractive for handling an interactive annotation procedure since it is convenient to update based on novel annotations. However, a single GBT has appears to be unable to generalize to a set of pictures.