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Found 4 papers, 2 papers with code
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT
DL methods can be designed for AIS lesion detection on CT using the vast quantities of routinely-collected CT brain scan data.
Challenges of building medical image datasets for development of deep learning software in stroke
Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited.
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
Segmentation masks of pathological areas are useful in many medical applications, such as brain tumour and stroke management.
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
We investigate the best way to generate the saliency maps employed in our architecture and propose a way to obtain them from adversarially generated counterfactual images.
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