Buch, Englisch, 352 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g
Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVI
Buch, Englisch, 352 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-91720-2
Verlag: Springer Nature Switzerland
The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024.
These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
.- Fine-tuning a Multiple Instance Learning Feature Extractor with Masked Context Modelling and Knowledge Distillation.
.- Advancing Medical Radiograph Representation Learning: A Hybrid Pretraining Paradigm with Multilevel Semantic Granularity.
.- Can virtual staining for high-throughput screening generalize?.
.- SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.
.- A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification.
.- Boosting Medical Image Registration Network Inherently via Collaborative Learning.
.- Genetic Information Analysis of Age-Related Macular Degeneration Fellow Eye Using Multi-Modal Selective ViT.
.- CHOTA: A Higher Order Accuracy Metric for Cell Tracking.
.- Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification.
.- Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images.
.- BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans.
.- Affinity-VAE: incorporating prior knowledge in representation learning from scientific images.
.- Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models.
.- Going Beyond U-Net: Assessing Vision Transformers for Semantic Segmentation in Microscopy Image Analysis.
.- SS-MIL: Attention-Based Selective Correlated Multiple Instance Learning for Whole Slide Image Classification.
.- MicroSSIM: Improved Structured Similarity for Comparing Microscopy Data.
.- Generalized Segmentation for Maxillary Sinus and Mandibular Canal in Dental Panoramic X-rays.
.- MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation.
.- NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful.
.- Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics.
.- A Novel Approach to Linking Histology Images with DNA Methylation.
