Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining

• URL: http://arxiv.org/abs/2506.04950v1
• Date: Thu, 05 Jun 2025 12:22:03 GMT
• Title: Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining
• Authors: Yong Sun, Yipeng Wang, Junyu Shi, Zhiyuan Zhang, Yanmei Xiao, Lei Zhu, Manxi Jiang, Qiang Nie,
• Abstract summary: We propose Video-Based Embryo Grading - the first paradigm that directly utilizes full-length time-lapse monitoring (TLM) videos to predict embryologists' overall quality assessments.<n>To support this task, we curate a real-world clinical dataset comprising over 2,500 TLM videos, each annotated with a grading label indicating the overall quality of embryos.<n>This work provides a valuable methodological framework for AI-assisted embryo selection.
• Score: 24.521943129885347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence has recently shown promise in automated embryo selection for In-Vitro Fertilization (IVF). However, current approaches either address partial embryo evaluation lacking holistic quality assessment or target clinical outcomes inevitably confounded by extra-embryonic factors, both limiting clinical utility. To bridge this gap, we propose a new task called Video-Based Embryo Grading - the first paradigm that directly utilizes full-length time-lapse monitoring (TLM) videos to predict embryologists' overall quality assessments. To support this task, we curate a real-world clinical dataset comprising over 2,500 TLM videos, each annotated with a grading label indicating the overall quality of embryos. Grounded in clinical decision-making principles, we propose a Complementary Spatial-Temporal Pattern Mining (CoSTeM) framework that conceptually replicates embryologists' evaluation process. The CoSTeM comprises two branches: (1) a morphological branch using a Mixture of Cross-Attentive Experts layer and a Temporal Selection Block to select discriminative local structural features, and (2) a morphokinetic branch employing a Temporal Transformer to model global developmental trajectories, synergistically integrating static and dynamic determinants for grading embryos. Extensive experimental results demonstrate the superiority of our design. This work provides a valuable methodological framework for AI-assisted embryo selection. The dataset and source code will be publicly available upon acceptance.

Related papers

• Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification [50.899861205016265]
  We propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification.<n>Our framework introduces two key components into the common MIL model architecture.<n>We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets.
  arXiv  Detail & Related papers  (2025-03-08T04:51:58Z)
• Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology [6.418265127069878]
  We propose the use of omic embeddings during early and late fusion to capture complementary information from local (patch-level) to global (slide-level) interactions.<n>This dual fusion strategy enhances interpretability and classification performance, highlighting its potential for clinical diagnostics.
  arXiv  Detail & Related papers  (2024-11-26T13:25:53Z)
• Multimodal Learning for Embryo Viability Prediction in Clinical IVF [24.257300904706902]
  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 process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process.
  arXiv  Detail & Related papers  (2024-10-21T01:58:26Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment [11.33138866472943]
  General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA. We propose a semi-supervised model, termed SiamParseNet (SPN), which consists of two branches, one for intra-frame body parts segmentation and another for inter-frame label propagation.
  arXiv  Detail & Related papers  (2022-10-14T18:46:30Z)
• Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network [5.541644538483947]
  The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. Recent video-based GMs classification has attracted attention, but this approach would be strongly affected by irrelevant information. We propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information.
  arXiv  Detail & Related papers  (2022-07-04T05:21:09Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• Developmental Stage Classification of EmbryosUsing Two-Stream Neural Network with Linear-Chain Conditional Random Field [74.53314729742966]
  We propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We demonstrate our algorithm on two time-lapse embryo video datasets.
  arXiv  Detail & Related papers  (2021-07-13T19:56:01Z)
• Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences [0.0]
  We investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model.
  arXiv  Detail & Related papers  (2021-03-12T13:36:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.