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Found 6 papers, 3 papers with code
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models
We thoroughly evaluate the impact of the employed generative models on state-of-the-art neural networks in terms of accuracy, convergence speed and ensembling.
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentation.
Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models
Motivated by this, we analyse cataract surgery video data for the worst-performing phases of a pre-trained downstream tool classifier.
Federated Stain Normalization for Computational Pathology
As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology.
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation.
FrOoDo: Framework for Out-of-Distribution Detection
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology.
