4 citations · 6 across the 5 of their papers we have counts for
5 papers
Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos
Zhiyi Shi, Junsik Kim, Helen Y. Yang +5
Automating embryo viability prediction for in vitro fertilization (IVF) is important but challenging due to the limited availability of labeled pregnancy outcome data, as only a sm…
Multimodal Learning for Embryo Viability Prediction in Clinical IVF
Junsik Kim, Zhiyi Shi, Davin Jeong +8
In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this p…
Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field
Stanislav Lukyanenko, Won-Dong Jang, Donglai Wei +8
The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. F…
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
Won-Dong Jang, Donglai Wei, Xingxuan Zhang +6
Blastomere instance segmentation is important for analyzing embryos' abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. A…
Automated Measurements of Key Morphological Features of Human Embryos for IVF
Brian D. Leahy, Won-Dong Jang, Helen Y. Yang +11
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse micr…