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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29October 4, 2024, Proceedings, Part XVI [Pehme köide]

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  • Formaat: Paperback / softback, 352 pages, kõrgus x laius: 235x155 mm, 116 Illustrations, color; 7 Illustrations, black and white; LV, 352 p. 123 illus., 116 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15638
  • Ilmumisaeg: 30-May-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031917200
  • ISBN-13: 9783031917202
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Computer Vision ECCV 2024 Workshops: Milan, Italy, September 29October 4, 2024, Proceedings, Part XVISuurem pilt
  • Formaat: Paperback / softback, 352 pages, kõrgus x laius: 235x155 mm, 116 Illustrations, color; 7 Illustrations, black and white; LV, 352 p. 123 illus., 116 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15638
  • Ilmumisaeg: 30-May-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031917200
  • ISBN-13: 9783031917202
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031917202.html
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 29October 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.
.- 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.