###### Abstract
Images with no mask annotations are given a pseudo mask ground truth generated by the initially trained model and the second model is trained to perform both segmentation and image level classification with these pseudo annotations. Generative adversarial networks (GANs) are tuned to perform semantic segmentation while using both image level annotations and generate mask annotations. [PERSON] et al. [10] uses both bounding box annotations and image level labels to localize and classify objects using multiple-instance learning (MIL). Images without bounding box annotations are given a bounding box chosen from a bag of bounding boxes presented during the localization stage. Various methods for choosing an object among the candidates are tested. Active learning is a mechanism for expanding the given dataset by labeling unlabeled data with the train model. User intervention for labeling is encouraged during the whole training process. Active learning can be applied to different types of datasets and fields where data is scarce. Mask prediction for lung CT images generated by unsupervised segmentation is used as ground truth annotation for training a supervised segmentation network [13]. The segmentation network is trained multiple times while using the mask prediction from the previous model as the ground truth, progressively improving after each training session. We propose an appropriate method for controlling the influence of weakly labeled data in a Faster-RCNN based object detection model. The presented method shows increase in correct localization (CorLoc) measures, which is preferred over mean average precision (mAP) in medical imaging, and fraction of lesions detected, which measures the localization performance. The presented method assumes a relatively small strongly annotated dataset insufficient for achieving high classification capability and a larger dataset with weakly labeled images, which is a typical setting for medical imaging where making strong annotations are costly. The main contributions of this work are, first, designing a reasonable method of controlling the effect of weakly labeled data in an end-to-end object detection model and, second, designing an acceptable approach for actively assigning annotations for weakly labeled data, supplementing the insufficient annotations for object detection. The strongly annotated data, \(D_{strong}\), contain a single bounding box coordinate and the box classification label per image, and the weakly labeled data, \(D_{weak}\), only contain an image level label per image. An actively annotated dataset, \(D_{active}\), is newly constructed after a training session and will be concatenated to \(D_{strong}\) in the next training session. Individual data streams are maintained during training for the strongly annotated dataset and the weakly labeled dataset. Dataflow in the network is shown in Figure 1. The loss for \(D_{strong}\) is calculated in the same manner, as it is proposed in [11], where loss for the region proposal network (RPN) and the RCNN-top layer is propagated seperately. Images in the \(D_{weak}\) dataset can contribute to the classification loss in RCNN-top only when the RPN has proposed a correct region. The loss for \(D_{weak}\) will have less influence until this condition is believed to be satisfied. After the first training session is finished, \(D_{active}\) dataset is crafted from \(D_{weak}\) by giving a prediction that is likely to contain a mass a single ground truth annotation. Images in \(D_{active}\) will be concatenated to \(D_{strong}\), reducing the sparsity issue that the task originally conveyed. The experiments show that using \(D_{weak}\) images in a conservative manner helps the classifier to be detect more lesions. Training with \(D_{active}\) shows an additional increase in the overall performance. We believe that the proposed method can be adopted to general cases where strong annotations are insufficient to train the model classifier and weak labels are more available.