Benefits of Linear Conditioning with Metadata for Image Segmentation
Polytechnique Montreal（蒙特利尔理工学院）；Mila, Quebec AI Institute（魁北克人工智能研究所米拉）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；Stanford University Center for Artificial Intelligence in Medicine & Imaging（斯坦福大学医学与影像人工智能中心）；University of Montreal（蒙特利尔大学）
Andreanne Lemay, Charley Gros, Olivier Vincent, Yaou Liu, Joseph Paul Cohen, Julien Cohen-Adad
Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This metadata is usually disregarded by image segmentation methods. In this work, we adapt a linear conditioning method called FiLM (Feature-wise Linear Modulation) for image segmentation tasks. This FiLM adaptation enables integrating metadata into segmentation models for better performance. We observed an average Dice score increase of 5.1% on spinal cord tumor segmentation when incorporating the tumor type with FiLM. The metadata modulates the segmentation process through low-cost affine transformations applied on feature maps which can be included in any neural network's architecture. Additionally, we assess the relevance of segmentation FiLM layers for tackling common challenges in medical imaging: multi-class training with missing segmentations, model adaptation to multiple tasks, and training with a limited or unbalanced number of annotated data. Our results demonstrated the following benefits of FiLM for segmentation: FiLMed U-Net was robust to missing labels and reached higher Dice scores with few labels (up to 16.7%) compared to single-task U-Net. The code is open-source and available at www.ivadomed.org.
Metadata Improves Segmentation Through Multitasking Elicitation
Ukrainian Catholic University（乌克兰天主教大学）；Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)（莱布尼茨植物遗传与作物研究所（IPK））；University of Tartu（塔尔图大学）；Revvity, Inc.（Revvity公司）；Wellcome Sanger Institute（威康桑格研究所）
Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification
Department of Computer Science, University of Verona（维罗纳大学计算机科学系）；Institute of Marine Research（海洋研究所）；University of Agder, Centre for Coastal Research（阿格德大学海岸研究中心）
Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning
Department of Psychology & Neuroscience, Auckland University of Technology（心理学与神经科学系，奥克兰技术大学）；Department of Psychiatry, University of Melbourne（精神病学系，墨尔本大学）
MonoPRIO: Adaptive Prior Conditioning for Unified Monocular 3D Object Detection
organization= Department of Computer Science, Loughborough University , addressline= Epinal Way , city= Loughborough , postcode= LE11 3TU , state= Leicestershire , country= United Kingdom；organization= European Center for Risk \& Resilience Studies, University of Southern Denmark , addressline= Degnevej 14 , city= Esbjerg , postcode= 6705 , country= Denmark
Metadata-Aware Adaptation of a Generative Foundation Model for Conditional CMR Synthesis
Facultat de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与信息学院）；NIHR Barts Biomedical Research Centre（英国国立卫生研究院巴特生物医学研究中心）；St Bartholomew’s Hospital（圣巴塞洛缪医院）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究学院）
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
Imperial College London（帝国理工学院）；Medical University of Vienna（维也纳医科大学）；University of Michigan（密歇根大学）；King’s College London（伦敦国王学院）；Institute of Molecular and Clinical Ophthalmology Basel（巴塞尔分子与临床眼科研究所）；Universitat Basel（巴塞尔大学）；University College London（伦敦大学学院）；Moorfields Eye Hospital（摩尔菲尔兹眼科医院）；University of Southampton（南安普顿大学）
Latent Space Conditioning on Generative Adversarial Networks
Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；University of Heidelberg（海德堡大学）；Fraunhofer Center Machine Learning（弗劳恩霍夫机器学习中心）；University of Mannheim（曼海姆大学）；Offenburg University（奥芬堡大学）
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient
University of Macau（澳门大学）；Great Bay University（大湾区大学）；Great Bay Institute for Advanced Study（大湾区高等研究院）；University of Science and Technology of China（中国科学技术大学）
A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data
University of Kentucky（肯塔基大学）；Department of Computer Science, University of Kentucky（肯塔基大学计算机科学系）；Department of Neurological Surgery, University of Kentucky（肯塔基大学神经外科学系）；University of Kentucky College of Medicine（肯塔基大学医学院）；Department of Neurology, University of Kentucky（肯塔基大学神经学系）
