Garcia, Nuno CruzSantos, Rodrigo da Silva e2025-01-292025-01-2920252024http://hdl.handle.net/10400.5/97909Tese de mestrado, Engenharia Informática, 2025, Universidade de Lisboa, Faculdade de CiênciasComputer-Aided Diagnosis is crucial in pathology, notably with the introduction of whole slide imaging (WSI). Deep learning excels in WSI analysis, yet challenges persist due to high resolution (e.g., 100,000 x 100,000 pixels) and labor-intensive pixel-level annotations. Recent advancements in Deep Learning have proven to be effective to address these issues. More specifically, Multiple Instance Learning (MIL) and Self-Supervised Learning (SSL) are the current state-of-the-art methods to overcome the limitations associated with WSI analysis. MIL is ideally suited for histopathology image analysis, as it enables to learn from just WSI labels without requiring the need for pixel-level annotation. Additionally, SSL has proven to excel at extracting rich and meaningful representations from large unlabeled datasets which is specifically well-suited for high resolution WSI since it doesn’t demand pixel-level annotations. This work is build on a prior architecture that uses both methods, in which we extend the research by incorporating other state-of-the-art SSL techniques that outperform the current SSL method employed, such as MoCo, DINO and SwAV. We trained from scratch some of the SSL models implemented, while the others, we used their pre-trained version due to computational bottlenecks. Even though there is downsides regarding using pre-trained models in which were pre-trained on different domains, some of the best results we got was from pre-trained models, proving once more that SSL is a promising technique when dealing with lack of annotations.engAprendizagem Auto-SupervisionadaAprendizagem de Instâncias MúltiplasAprendizagem ProfundaImages HistopatológicasImagens de Lâmina CompletaTeses de mestrado - 2025master thesis