Self-supervised model that performs single-frame digital-subtraction-angiography-style vessel/background separation directly from a single live (non-subtracted) coronary angiogram frame, then supports fine-tuned coronary vessel segmentation. A U-Net-style network is pretrained via an image-to-image translation objective on 58,128 unannotated angiography DICOM series (3,756 patients), then fine-tuned for vessel segmentation on just 40 expert-annotated frames, reaching a Dice of 0.828 on the held-out fine-tuning set and a new state-of-the-art Dice of 0.755 on the public XCAD benchmark. Intended to help clinicians visualize potential stenosis sites without requiring true two-frame digital subtraction acquisition.

Reconstructs digital 12-lead ECG waveforms from scanned or photographed paper printouts, using an nnU-Net image segmentation model to trace the signal pixels followed by a Hough-transform-based reconstruction pipeline. This is a digitization tool rather than a diagnostic model - it recovers a usable signal from a paper record rather than producing a diagnosis. Won the PhysioNet/Computing in Cardiology Challenge 2024; developed by a team at the University of Oxford.

Translates a raw PPG waveform into a full continuous arterial blood-pressure waveform, from which systolic, diastolic, and mean arterial pressure are derived. Uses a two-stage cascaded 1D convolutional network in a U-Net style, with a coarse approximation stage followed by a refinement stage. Meets BHS Grade A and AAMI accuracy standards for diastolic and mean arterial pressure. Developed at BUET.

Generative adversarial network that synthesizes realistic 10-second, 12-lead normal-sinus-rhythm ECGs from scratch, without using any real patient data at inference time, enabling privacy-preserving data sharing and augmentation. Uses a U-Net-style 1D deconvolutional generator with a WaveGAN-inspired discriminator. Outperformed a WaveGAN* baseline on the fraction of generated tracings classified as normal sinus rhythm by a commercial ECG interpretation algorithm. Developed by SimulaMet and Oslo Metropolitan University.