no code implementations • 15 Dec 2023 • Pedro Osorio, Guillermo Jimenez-Perez, Javier Montalt-Tordera, Jens Hooge, Guillem Duran-Ballester, Shivam Singh, Moritz Radbruch, Ute Bach, Sabrina Schroeder, Krystyna Siudak, Julia Vienenkoetter, Bettina Lawrenz, Sadegh Mohammadi
Finally, we show that synthetic data effectively trains AI models.
no code implementations • 24 Aug 2023 • Josef Cersovsky, Sadegh Mohammadi, Dagmar Kainmueller, Johannes Hoehne
The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine.
no code implementations • 22 Apr 2022 • Tuan Truong, Matthias Lenga, Antoine Serrurier, Sadegh Mohammadi
Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0. 8658, a sensitivity of 0. 8057, and a specificity of 0. 7958.
no code implementations • 23 Aug 2021 • Tuan Truong, Sadegh Mohammadi, Matthias Lenga
In addition, we introduce Dynamic Visual Meta-Embedding (DVME) as an end-to-end transfer learning approach that fuses pretrained embeddings from multiple models.
no code implementations • ICCV 2017 • Alessandro Perina, Sadegh Mohammadi, Nebojsa Jojic, Vittorio Murino
In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.