  Although CNNs are widely considered as the state-of-the-art models in various
applications of image analysis, one of the main challenges still open is the
training of a CNN on high resolution images. Different strategies have been
proposed involving either a rescaling of the image or an individual processing
of parts of the image. Such strategies cannot be applied to images, such as
gigapixel histopathological images, for which a high reduction in resolution
inherently effects a loss of discriminative information, and in respect of
which the analysis of single parts of the image suffers from a lack of global
information or implies a high workload in terms of annotating the training
images in such a way as to select significant parts. We propose a method for
the analysis of gigapixel histopathological images solely by using weak
image-level labels. In particular, two analysis tasks are taken into account: a
binary classification and a prediction of the tumor proliferation score. Our
method is based on a CNN structure consisting of a compressing path and a
learning path. In the compressing path, the gigapixel image is packed into a
grid-based feature map by using a residual network devoted to the feature
extraction of each patch into which the image has been divided. In the learning
path, attention modules are applied to the grid-based feature map, taking into
account spatial correlations of neighboring patch features to find regions of
interest, which are then used for the final whole slide analysis. Our method
integrates both global and local information, is flexible with regard to the
size of the input images and only requires weak image-level labels. Comparisons
with different methods of the state-of-the-art on two well known datasets,
Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed
model.
