Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

Manifest

rom the thematic hook “organization and knowledge” to the various theoretical perspectives. The concept of knowledge has gained much popularity in the organizational discourse of recent years, both in the context of popular and practically oriented management and organizational doctrines as well as

ARK

Yiming Qian,Liangzhi Li,Huazhu Fu,Meng Wang,Qingsheng Peng,Yih Chung Tham,Chingyu Cheng,Yong Liu,Ricen und damit in der Verwendung von Macht kommt: Dabei gewinnt vor allem die Orientierung an Person und Gruppe massiv an Bedeutung. Auf eine Kurzformel gebracht bedeutet dies: Erfolgreiche Führung muss neben der Organisationsdynamik eine Expertise für die Gruppendynamik entwickeln. Allerdings stehen

不透明性

細(xì)頸瓶

SLPT: Selective Labeling Meets Prompt Tuning on?Label-Limited Lesion Segmentationbel-limited scenarios can lead to overfitting and suboptimal performance. Recently, prompt tuning has emerged as a more promising technique that introduces a few additional tunable parameters as prompts to a task-agnostic pre-trained model, and updates only these parameters using supervision from li

合并

COLosSAL: A Benchmark for?Cold-Start Active Learning for?3D Medical Image Segmentationmance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the enti

刪減

Cholesterol

incisive

PLD-AL: Pseudo-label Divergence-Based Active Learning in?Carotid Intima-Media Segmentation for?Ultraod that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Act