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Found 5 papers, 2 papers with code
Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images
In this work, models were trained for simultaneous prediction of multiple DNA alterations from H&E images using a multi-task approach.
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images
As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing and becoming eligible for immunotherapy.
Make-A-Video: Text-to-Video Generation without Text-Video Data
We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V).
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
Altogether, MUGEN can help progress research in many tasks in multimodal understanding and generation.
Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging
Deep transfer learning using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown strong predictive power in characterization of breast lesions.
