Medical Image Learning with Limited and Noisy Data [[electronic resource] ] : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang

• Record Nr.: UNISA996558470503316
• Autore: Xue Zhiyun
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• ISBN: 3-031-44917-7
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (274 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 14307
• Altri autori (Persone): AntaniSameer; ZamzmiGhada; YangFeng; RajaramanSivaramakrishnan; HuangSharon Xiaolei; LinguraruMarius George; LiangZhaohui
• Disciplina: 006
• Soggetti: Image processing - Digital techniques; Computer vision; Computer Imaging, Vision, Pattern Recognition and Graphics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Efficient Annotation and Training Strategies -- Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations -- ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation -- Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images -- Affordable Graph Neural Network Framework using Topological Graph Contraction -- Approaches for Noisy, Missing, and Low Quality Data -- Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT -- A Multitask Framework for Label Refinement and Lesion ; Segmentation in Clinical Brain Imaging -- COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation -- Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Image -- Unsupervised, Self-supervised, and Contrastive Learning -- Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection -- FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation -- Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy -- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning -- SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction -- Robust Unsupervised Image to Template Registration Without Image Similarity Los -- A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue -- Weakly-supervised, Semi-supervised, and Multitask Learning -- Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification -- Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework -- Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation -- Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image -- Active Learning -- Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach -- Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation -- Active Transfer Learning for 3D Hippocampus Segmentation -- Transfer Learning -- Using Training Samples as Transitive Information Bridges in Predicted 4D MRI -- To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology -- Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks.
• Sommario/riassunto: This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.

Changement climatique : Un défi pour les ingénieurs / / Jean-François Coste, Antoine Coursimault, Dominique Chauvin, Florent Brissaud, Jacques Bongrand; IESF

• Record Nr.: UNINA9910265244003321
• Autore: Brissaud Florent
• Pubbl/distr/stampa: EDP SCIENCES, 2018; Les Ulis : , : EDP Sciences, , [2021]; ©2018
• ISBN: 2-7598-2250-8
• Descrizione fisica: 1 online resource (104 p.)
• Disciplina: 550
• Soggetti: SCIENCE / Physics / General
• Lingua di pubblicazione: Francese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Frontmatter -- SOMMAIRE -- Préface -- Avertissement -- Résumé -- Introduction -- Les prévisions du GIEC -- Les émissions de gaz à effet de serre (GES) -- La COP21 -- Quelques considérations économiques -- Les engagements par pays : Intended Nationally Determined Contribution (INDC) -- Propositions de la Commission de l'Union européenne -- La France est-elle sur le bon chemin ? -- Annexes
• Sommario/riassunto: Les scientifiques et les ingénieurs ont un rôle majeur à jouer pour l'avenir de l'humanité : ce sont eux qui ont pu alerter le monde sur le danger que court le climat de la planète et sur ses conséquences ; ce sont eux qui en ont identifié une cause très probable et sur laquelle nous pouvons réagir. Notre action dépend de nombreux facteurs : prise de conscience universelle, volontés politiques, solidarité entre les Etats et les peuples, moyens financiers. mais aussi in fine moyens scientifiques et techniques. Cependant, les ingénieurs ne sont pas des magiciens. Remplacer l'usage des ressources énergétiques traditionnelles, stockées depuis des millions d'années, par des ressources non carbonées, autant que possible renouvelables (nucléaire, hydraulique, géothermie, biomasse, vent, soleil...) demande du temps. L'objet de ce livre est double : montrer d'une part l'attention que les ingénieurs et scientifiques portent à cette menace fondamentale et leur compréhension du problème, d'autre part leur ; souhait de participer davantage aux débats et aux décisions, avec l'espoir d'aboutir à des orientations et des réalisations plus efficaces en faveur de l'objectif recherché. Les auteurs estiment que par leur indépendance, leur objectivité, leurs compétences et leur pragmatisme, les ingénieurs devraient contribuer au rapprochement entre rêve et réalité.