Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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CS,: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Interv performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found

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Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Enn Wireless Capsule Endoscopy (WCE) images. However, the CAM labels tend to be extremely noisy, and there is an irreparable gap between CAM labels and ground truths for medical images. This paper proposes a new Discrepancy-basEd Active Learning (DEAL) approach to bridge the gap between CAMs and groun

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Diffusion Models for?Medical Anomaly Detection Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion