AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
arXiv:2511.18454v3 Announce Type: replace-cross Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF). However, manual grading is subjective and inefficient, while existing deep learning solutions often lack clinical explainability or suffer from accumulated errors in segmentation area estimation. To address these issues, this study proposes AttnRegDeepLab (Attention-Guided Regression DeepLab), a framework characterized by dual-branch Multi-Task Learning (MTL). A vanilla DeepLabV3+ decoder is modified by inte
The increasing integration of AI in healthcare, particularly in diagnostic processes, is driving the need for more interpretable and robust solutions to overcome limitations of previous models.
This development represents a step towards mitigating subjectivity and inefficiency in critical medical procedures like IVF, potentially improving success rates and standardizing evaluation processes.
The ability to provide interpretable AI for medical grading enhances trust and clinical utility, moving AI from a black box to a more actionable diagnostic tool in sensitive areas.
- · Fertility clinics
- · Patients undergoing IVF
- · Medical AI developers
- · Biomedical researchers
- · Human embryo graders (less critical role)
- · AI models lacking explainability
More standardized and accurate embryo grading in IVF procedures.
Increased success rates and reduced emotional burden for couples undergoing fertility treatments.
Potential for further AI applications in other subjective medical diagnostic areas requiring high interpretability and precision.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI