Until now, explorations into applying quantum computing to medical imaging remained largely theoretical calculations; ...

A novel sectional deep learning framework with region normalization improves hepatocellular carcinoma detection and staging ...

A benchmark of five transfer learning models on 8,000 field-collected grape leaf images showed top test accuracy reached only ...

Researchers use adversarial deep learning to reduce patient-specific bias in Raman spectroscopy while preserving 81% balanced accuracy in glioma IDH1 classification.

Natsume: I will be the one explaining today.Over the past 19 sessions, we have learned various mechanisms that support deep ...

Google Agile Deliberation helps define subjective visual concepts, lifting VLM classifier F1 by 10.5 points over zero-shot ...

A new contrastive-learning framework enables hyperspectral classification with few labels. It masks redundant spatial and spectral information, mixes ...

A new contrastive-learning framework enables hyperspectral classification with few labels. It masks redundant spatial and ...

Blur need no longer be a dead end. From GANscan and Blurryscope to his latest system, SCANIMUS, Michael Fanous explores how ...

My deep dive into deep learning was largely triggered by participating in a VLM competition hosted by REVAMP on the ...

Ganglberger and colleagues assessed a deep learning model’s ability to derive brain health scores from electroencephalography ...

A semi-supervised machine learning approach can cut the expert-labeling burden for beluga whale aerial surveys by five to ten times — and still outperform supervised baselines — according to a new ...