Robust Multi-Domain Mitosis Detection
원문 초록
Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a target representative feature space through unpaired image to image translation (CycleGAN). We comprehensively evaluate the performanceand usefulness by utilising the transformation to mitosis detection with candidate proposal and classification. This work presents a simple yet effective multi-step mitotic figure detection algorithm developed as a baseline for the MIDOG challenge. On the preliminary test set, the algorithm scoresan F1 score of 0.52.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
Cora: Correspondence-aware image editing using few step diffusion
Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing
Paired and Unpaired Image to Image Translation using Generative Adversarial Networks
Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.