Histopathology image analysis serves as the gold standard for diagnosis of cancer and is directly related to the subsequent therapeutic treatment. However, pixel-wise delineated annotations on whole slide images (WSIs) are time-consuming and tedious, which poses difficulties in building a large-scale training dataset. How to effectively utilize available whole slide image-level label, which can be easily acquired, for deep learning is quite appealing. The main barrier on this task is due to the heterogeneous patterns in fine magnification level but only the WSI-level labels are provided. Furthermore, a gigapixel scale WSI can not be easily analysed due to the immeasurable computational cost. In this paper, we propose a weakly supervised approach for fast and effective classification on whole slide lung cancer images. Our method takes advantage of a patch-based fully convolutional network for discriminative block retrieval. Furthermore, context-aware feature selection and aggregation strategies are proposed to generate globally holistic WSI descriptor. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin with accuracy of 97.1%. In addition, we highlight that a small number of available coarse annotations can contribute to further accuracy improvement. We believe that deep learning has great potential to assist pathologists for histology image diagnosis in the near future.