Search Results for author:
Found 5 papers, 0 papers with code
Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology
Finally, we show that synthetic data effectively trains AI models.
Towards Hierarchical Regional Transformer-based Multiple Instance Learning
The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine.
Fused Audio Instance and Representation for Respiratory Disease Detection
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.
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?
In addition, we introduce Dynamic Visual Meta-Embedding (DVME) as an end-to-end transfer learning approach that fuses pretrained embeddings from multiple models.
Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences
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.
