MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment

no code implementations • 15 Mar 2024 • Wenrui Fan, Mohammod Naimul Islam Suvon, Shuo Zhou, Xianyuan Liu, Samer Alabed, Venet Osmani, Andrew Swift, Chen Chen, Haiping Lu

Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.

Anatomy Contrastive Learning +1

Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning

no code implementations • 21 Dec 2023 • Michal K. Grzeszczyk, Tadeusz Satlawa, Angela Lungu, Andrew Swift, Andrew Narracott, Rod Hose, Tomasz Trzcinski, Arkadiusz Sitek

We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP.

Classification Feature Engineering +1

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

no code implementations • 1 Oct 2020 • Abdullah Sarhan, Ali Al-KhazÁly, Adam Gorner, Andrew Swift, Jon Rokne, Reda Alhajj, Andrew Crichton

Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma.

Optic Disc Segmentation Segmentation +1