Medical Image Learning with Limited and Noisy Data
Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Sorozatcím: Lecture Notes in Computer Science; 14307;
-
20% KEDVEZMÉNY?
- A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
- Kiadói listaár EUR 128.39
-
53 249 Ft (50 714 Ft + 5% áfa)
Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.
- Kedvezmény(ek) 20% (cc. 10 650 Ft off)
- Kedvezményes ár 42 600 Ft (40 571 Ft + 5% áfa)
Iratkozzon fel most és részesüljön kedvezőbb árainkból!
Feliratkozom
53 249 Ft
Beszerezhetőség
Megrendelésre a kiadó utánnyomja a könyvet. Rendelhető, de a szokásosnál kicsit lassabban érkezik meg.
Why don't you give exact delivery time?
A beszerzés időigényét az eddigi tapasztalatokra alapozva adjuk meg. Azért becsült, mert a terméket külföldről hozzuk be, így a kiadó kiszolgálásának pillanatnyi gyorsaságától is függ. A megadottnál gyorsabb és lassabb szállítás is elképzelhető, de mindent megteszünk, hogy Ön a lehető leghamarabb jusson hozzá a termékhez.
A termék adatai:
- Kiadás sorszáma 1st ed. 2023
- Kiadó Springer Nature Switzerland
- Megjelenés dátuma 2023. október 31.
- Kötetek száma 1 pieces, Book
- ISBN 9783031471964
- Kötéstípus Puhakötés
- Terjedelem270 oldal
- Méret 235x155 mm
- Súly 438 g
- Nyelv angol
- Illusztrációk XI, 270 p. 77 illus., 72 illus. in color. Illustrations, black & white 508
Kategóriák
Hosszú leírás:
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.
Tartalomjegyzék:
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 Knowingthe 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 forImproved 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.
Több
Quantum Theory and Measurement
20 065 Ft
18 059 Ft
Methods in Alcohol-Related Neuroscience Research
85 995 Ft
77 396 Ft
