eess.IV · 2026-06-25 · No. 34

Image and Video Processing, 2026-06-25.

2 new papers in eess.IV. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction

    Zahra Asghari Varzaneh, Reza Khoshkangini, Magnus Johnsson, Thomas Ebner, Lars Johansson

    eess.IV · cs.LG

    This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simultaneously in a single forward pass: segmentation of the ZP, TE, and ICM compartments, morphological grading, and implantation outcome prediction. Accurate blastocyst analysis in in vitro fertilization (IVF) is challenging. The compartments often have similar textures but very different...

    arxiv.org/abs/2606.25463 · PDF

  2. 02

    Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation

    Ümit Mert Çağlar, Alptekin Temizel

    eess.IV · cs.AI · cs.CV · cs.LG

    Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity. However, such metrics, including the widely...

    arxiv.org/abs/2606.25128 · PDF

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