arXiv AI
Jun 19
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
arXiv:2606. 20438v1 Announce Type: new Abstract: Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology.
Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation
Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches.
Related stories
arXiv AI
Sep 15
SpermYOLO: A Coordinated YOLO-Based Detector for Accurate and Efficient Sperm and Impurity Detection in Microscopic Images
arXiv:2609.14278v1 Announce Type: cross Abstract: Accurate sperm detection is essential for computer-assisted semen analysis, yet it remains challenging in microscopic images due to dense distributio...
arXiv Machine Learning
Jun 25
Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction
arXiv:2606. 25463v1 Announce Type: cross Abstract: This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis.
arXiv AI
Jul 10
Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
arXiv:2607. 08429v1 Announce Type: cross Abstract: Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation.
Hugging Face Trending Papers
Jul 27
Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.
arXiv Computer Vision
Sep 25
OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis
OncoVision is a privileged‑information training framework that learns from mammography images and clinical data during training but performs inference using only mammographic images. It employs an attention‑based encoder‑decoder to jointly segment masses, calcifications, axillary findings, and breast tissue, and predicts ten structured clinical features such as BI‑RADS. Two late‑fusion strategies (Independent and Dependent) integrate imaging, radiomic, and clinical information to improve diagnostic precision, and a retrospective multi‑reader study showed higher diagnostic confidence, reduced reading time, and segmentation accuracy comparable to or better than radiologists.
arXiv AI
Jun 2
MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registration
arXiv:2512. 17605v2 Announce Type: replace-cross Abstract: Robust mammography registration is essential for clinically relevant applications like tracking disease progression in breast tissue.
arXiv Machine Learning
Jul 3
Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health
arXiv:2607. 02127v1 Announce Type: cross Abstract: Penile measurement is clinically relevant across male reproductive and urogenital health, including conditions such as micropenis, congenital and endocrine disorders, and sexual or urinary dysfunction.
arXiv AI
Jun 4
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).
arXiv AI
Jun 24
Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting
arXiv:2606. 24390v1 Announce Type: cross Abstract: Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools.
arXiv Machine Learning
Jul 9
Compass: Prostate Cancer Detection Needs Multi-View Context
arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.
arXiv Computer Vision
Sep 1
Multimodal Shared Latent Representation of Narration, Microscope and iOCT Images for Phase Recognition in Vitreoretinal Surgery
arXiv:2608.31065v1 Announce Type: new Abstract: Surgical phase recognition is key to context-aware computer-assisted feedback in vitreoretinal procedures, yet the scarcity of synchronized multimodal...