bibtext: @InProceedings{ Yin_HistoSyn_MICCAI2024,
                   author = { Yin, Chong and Liu, Siqi and Wong, Vincent Wai-Sun and Yuen, Pong C. },
                   title = { { HistoSyn: Histomorphology-Focused Pathology Image Synthesis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Examining pathology images through visual microscopy is widely considered the most reliable method for diagnosing different medical conditions. Although deep learning-based methods show great potential for aiding pathology image analysis, they are hindered by the lack of accessible large-scale annotated data. Large text-to-image models have significantly advanced the synthesis of diverse contexts within natural image analysis, thereby expanding existing datasets. However, the variety of histomorphological features in pathology images, which differ from that of natural images, has been less explored. In this paper, we propose a histomorphology-focused pathology image synthesis (HistoSyn) method. Specifically, HistoSyn constructs instructive textural prompts from spatial and morphological attributes of pathology images. It involves analyzing the intricate patterns and structures found within pathological images and translating these visual details into descriptive prompts. Furthermore, HistoSyn presents new criteria for image quality evaluation focusing on spatial and morphological characteristics. Experiments have demonstrated that our method can achieve a diverse range of high-quality pathology images, with a focus on histomorphological attributes.
title: HistoSyn: Histomorphology-Focused Pathology Image Synthesis
authors: Yin, Chong Liu, Siqi Wong, Vincent Wai-Sun Yuen, Pong C.