44 citations · 49 across the 7 of their papers we have counts for
5 papers · 1 filter
QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging
Luca Zedda, Davide Antonio Mura, Cecilia Di Ruberto +4
Attention-based Multiple Instance Learning aggregators in medical imaging are prone to attention concentration, producing overconfident and unstable predictions. We introduce QG-MI…
Genetically Aligned Patient Representations Improve Hematological Diagnosis
Muhammed Furkan Dasdelen, Fatih Ozlugedik, Ilaria Looser +3
Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream diagnostic tasks. Hematologic…
CytoSAE: Interpretable Cell Embeddings for Hematology
Muhammed Furkan Dasdelen, Hyesu Lim, Michele Buck +3
Sparse autoencoders (SAEs) emerged as a promising tool for mechanistic interpretability of transformer-based foundation models. Very recently, SAEs were also adopted for the visual…
Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang +27
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3…
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboyina +13
GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal…