Histopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images.