March 11, 2025
July 21, 2024
al Hajj, Ghadi; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad; Widrich, Michael; Solberg, Anne H Schistad; Greiff, Victor; Pensar, Johan; Klambauer, Günter & Sandve, Geir Kjetil Ferkingstad
Deep learning methods, including deep multiple instance learning methods, have been criticized for their limited ability to incorporate domain knowledge. A reason that knowledge incorporation is challenging in deep learning is that the models usually lack a mapping between their model components and the entities of the domain, making it a non-trivial task to incorporate probabilistic prior information. In this work, we show that such a mapping between domain entities and model components can be defined for a multiple instance learning setting and propose a framework DeeMILIP that encompasses multiple strategies to exploit this mapping for prior knowledge incorporation. We motivate and formalize these strategies from a probabilistic perspective. Experiments on an immune-based diagnostics case show that our proposed strategies allow to learn generalizable models even in settings with weak signals, limited dataset size, and limited compute.
Incorporating probabilistic domain knowledge into deep multiple instance learning
al Hajj, Ghadi; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad; Widrich, Michael; Solberg, Anne H Schistad; Greiff, Victor; Pensar, Johan; Klambauer, Günter & Sandve, Geir Kjetil Ferkingstad
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:17279-17297
July 21, 2024
al Hajj, Ghadi; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad; Widrich, Michael; Solberg, Anne H Schistad; Greiff, Victor; Pensar, Johan; Klambauer, Günter & Sandve, Geir Kjetil Ferkingstad
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:17279-17297
July 21, 2024