  One of the most challenges in medical imaging is the lack of data and
annotated data. It is proven that classical segmentation methods such as U-NET
are useful but still limited due to the lack of annotated data. Using a weakly
supervised learning is a promising way to address this problem, however, it is
challenging to train one model to detect and locate efficiently different type
of lesions due to the huge variation in images. In this paper, we present a
novel approach to locate different type of lesions in positron emission
tomography (PET) images using only a class label at the image-level. First, a
simple convolutional neural network classifier is trained to predict the type
of cancer on two 2D MIP images. Then, a pseudo-localization of the tumor is
generated using class activation maps, back-propagated and corrected in a
multitask learning approach with prior knowledge, resulting in a tumor
detection mask. Finally, we use the mask generated from the two 2D images to
detect the tumor in the 3D image. The advantage of our proposed method consists
of detecting the whole tumor volume in 3D images, using only two 2D images of
PET image, and showing a very promising results. It can be used as a tool to
locate very efficiently tumors in a PET scan, which is a time-consuming task
for physicians. In addition, we show that our proposed method can be used to
conduct a radiomics study with state of the art results.
