Pathological images or biopsy images are samples of tissues from a specific location of a human or animal body. Pathological analysis is necessary whenever there are any lesions or any indicative symptoms for a certain disease, like cancer or the presence of bacteria in tissues. Nowadays, a large amount of biopsies per day is requested and sent for analysis by pathologists in order to make a diagnosis. This process can be difficult, time-consuming, and requires experience in detecting abnormal tissues. With the advances of technology, powerful scanners have been developed that have the ability to amplify 40× and digitize whole slide images, being able to see at the 250 µm scale. The state-of-the-art supervised Deep Learning methods applied to slide images classification or disease detection use mostly deep annotations (rich annotations), i.e. specific information where the disease is located if any. This dissertation aims to contribute with a semi-supervised architecture that enables models to be built, using Multiple Instance Learning and Online Hard Example Mining, from weakly-annotated (that inform whether the whole slide has or not the disease) datasets. The whole architecture presented in this dissertations consists of an application of these semi-supervised methods on a deep architecture with an attention module. The whole architecture is fit based on a set of biopsy images provided by Hospital da Luz (Lisbon), whose some instances contain helicobacter pylori, achieving an accuracy of 91.67% and capturing all positive ones.