4 accepted papers
Accurate cancer risk assessment is critical for personalized treatment planning. While multimodal models that integrate histopathology with complementary data modalities (e.g., genomics, or clinical reports) exhibit superior prognostic capability, they typically assume full data availability, an unr
The complexity of EEG data presents significant challenges for accurate diagnosis in neurological conditions such as Alzheimer’s disease. In this paper, we introduce Explainable Orthogonal Attention Networks, a novel approach for EEG-based analysis that decouples spatial and temporal features to mor…
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering molecular impact on the cell. In this work, we lear…
Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore…