1.

Record Nr.

UNINA9910461563703321

Autore

Schwöbel Christine E. J

Titolo

Global constitutionalism in international legal perspective [[electronic resource] /] / by Christine E.J. Schwöbel

Pubbl/distr/stampa

Leiden [Netherlands] ; ; Boston, : Martinus Nijhoff Publishers, 2011

ISBN

1-283-12016-X

9786613120168

90-04-19522-X

Descrizione fisica

1 online resource (217 p.)

Collana

Queen Mary studies in international law, , 1877-4822 ; ; v. 4

Disciplina

341/.1

Soggetti

International law

Constitutional law

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Dimensions of global constitutionalism in public international law -- A historical analysis of key themes of global constitutionalism -- Questioning the contributions of public international law to the debate on global constitutionalism -- A suggestion for a reorientation of the debate towards organic global constitutionalism -- A practical approach to organic global constitutionalism.

Sommario/riassunto

The question of whether a global constitution exists or is emerging, and if so, what form it takes, is one of the most intriguing and controversial topics of recent international theory. This book examines public international law contributions to the debate, specifically taking a step back to enquire about the underlying assumptions that inform this debate. While contemporary contributors declare the idea of global constitutionalism to be global, this book reveals and interrogates the underlying liberal democratic themes that define prevailing approaches, thus calling universality into question. Drawing on critical theories within and without the international legal discipline, this book suggests a reconceptualisation of global constitutionalism in terms of what is named ‘organic global constitutionalism’. The book thus addresses significant shortcomings and illuminates necessary reorientations to a



field that is currently still in the crucial phase of formation.

2.

Record Nr.

UNISA996691665903316

Autore

Bhattarai Binod

Titolo

Data Engineering in Medical Imaging : Third MICCAI Workshop, DEMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings / / edited by Binod Bhattarai, Anita Rau, Razvan Caramalau, Annika Reinke, Anh Nguyen, Ana Namburete, Prashnna Gyawali, Danail Stoyanov

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026

ISBN

3-032-08009-6

9783032080097

Edizione

[1st ed. 2026.]

Descrizione fisica

1 online resource (430 pages)

Collana

Lecture Notes in Computer Science, , 1611-3349 ; ; 16191

Altri autori (Persone)

RauAnita

CaramalauRazvan

ReinkeAnnika

Nguyẽ̂nAnh

NambureteAna

GyawaliPrashnna

StoyanovDanail

Disciplina

620.00285

Soggetti

Engineering - Data processing

Image processing - Digital techniques

Computer vision

Data Engineering

Computer Imaging, Vision, Pattern Recognition and Graphics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

-- Surgical Vision World Model.  -- Overcoming Data Scarcity: Brain Tumor Segmentation in Pediatric and African Populations.  -- PiMPiC: An Overlap-Aware Contrastive Learning Framework for 3D Patch-Based Medical Image Segmentation.  -- DiffusionXRay: A Diffusion and GAN-Based Approach for Enhancing Digitally Reconstructed Chest



Radiographs.  -- SingleStrip: learning skull-stripping from a single labeled example.  -- Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy.  -- Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation.  -- Enhancing Malaria-Infected Red Blood Cell Detection with Domain-Aware Generative Augmentation.  -- Analysis of Transferability Estimation Metrics for Surgical Phase Recognition.  -- Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis.  -- Lesion-Aware Visual-Language Fusion for Automated Image Captioning of Ulcerative Colitis Endoscopic Examinations.  -- Zero-shot Monocular Metric Depth for Endoscopic Images.  -- Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy.  -- RadSURF: Automated Synthesis of Radiographs and Surface Representation of Vertebrae for Single View Reconstruction.  -- A Dataset and Benchmark for Enhancing Retained Foreign Object Detection Through Physics-based Image Synthesis.  -- Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis.  -- Robust Early Detection of Barrett’s Neoplasia: Addressing Low-Prevalence Challenges with Generative Modeling.  -- Instance-Balanced Patch Sampling for Whole-Body Lesion Segmentation.  -- Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from Clinical CT Head Scans.  -- Estimating 2D Keypoints of Surgical Tools Using Vision-Language Models with Low-Rank Adaptation.  -- Exploring Pre-training Across Domains for Few-Shot Surgical Skill Assessment.  -- Effect of Data Augmentation on Conformal Prediction for Diabetic Retinopathy.  -- Addressing Bias in VLMs for Glaucoma Detection Without Protected Attribute Supervision.  -- Balancing Redundancy and Diversity: An In-Depth Analysis of Active Learning for Laparoscopic Video Segmentation.

Sommario/riassunto

This book constitutes the proceedings of the Third MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2025, held in conjunction with the 28th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, on September 27, 2025. The 24 full papers included in this book were carefully reviewed and selected from 33 submissions. These papers focus on the topic of data engineering in medical imaging and address open questions in the field. The workshop welcomes various approaches such as data and label augmentation, active learning and active synthesis, federated learning, multimodal learning, self-supervised learning, and large-scale data management and data quality assessment.