bibtext: @InProceedings{ Hej_Conditional_MICCAI2024,
                   author = { Hejrati, Behzad and Banerjee, Soumyanil and Glide-Hurst, Carri and Dong, Ming },
                   title = { { Conditional diffusion model with spatial attention and latent embedding for medical image segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/.
title: Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
authors: Hejrati, Behzad Banerjee, Soumyanil Glide-Hurst, Carri Dong, Ming