Computer Vision - ECCV 2024 Workshops : Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVI (Lecture Notes in Computer Science)

個数:

Computer Vision - ECCV 2024 Workshops : Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVI (Lecture Notes in Computer Science)

  • 在庫がございません。海外の書籍取次会社を通じて出版社等からお取り寄せいたします。
    通常6~9週間ほどで発送の見込みですが、商品によってはさらに時間がかかることもございます。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合がございます。
    2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
    3. 美品のご指定は承りかねます。

    ●3Dセキュア導入とクレジットカードによるお支払いについて
  • 【入荷遅延について】
    世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
    おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • ◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
    また、洋書販売価格は、ご注文確定時点での日本円価格となります。
    ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。
  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 352 p.
  • 商品コード 9783031917202

Full Description

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.

Contents

.- 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.

最近チェックした商品