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Produktbild: Medical Image Understanding and Analysis
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Medical Image Understanding and Analysis 29th Annual Conference, MIUA 2025, Leeds, UK, July 15–17, 2025, Proceedings, Part III

Aus der Reihe Lecture Notes in Computer Science
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79,99 € UVP 98,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.07.2025

Abbildungen

XIII, 99 illus., 92 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen

Herausgeber

Sharib Ali + weitere

Verlag

Springer

Seitenzahl

336

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

534 g

Sprache

Englisch

ISBN

978-3-031-98693-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.07.2025

Abbildungen

XIII, 99 illus., 92 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen

Herausgeber

Verlag

Springer

Seitenzahl

336

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

534 g

Sprache

Englisch

ISBN

978-3-031-98693-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Medical Image Understanding and Analysis

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    .- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation.

    .- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis.

    .- Promptable Cancer Segmentation Using Minimal Expert-curated Data.

    .- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.

    .- Semantic Segmentation with Spreading Scribbles.

    .- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology.

    .- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models.

    .- Segmentation in Histopathology Utilising Simulated Masked Patches.

    .- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation.

    .- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation.

    .- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer.

    .- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation.

    .- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation.

    .- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery.

    .- Retinal and Vascular Image Analysis.

    .- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations.

    .- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis.

    .- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage.

    .- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration.

    .- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation.

    .- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis.

    .- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation.

    .- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.