3.1k citations · 7.2k across the 83 of their papers we have counts for
84 papers
Text-Driven Tumor Synthesis
Xinran Li, Yi Shuai, Chen Liu +11
Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However, existing synthesis methods, wh…
VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge
Vishwesh Nath, Wenqi Li, Dong Yang +22
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is esse…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
MAISI: Medical AI for Synthetic Imaging
Pengfei Guo, Can Zhao, Dong Yang +8
Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an…
A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation
Yufan He, Pengfei Guo, Yucheng Tang +7
Since the release of Segment Anything 2 (SAM2), the medical imaging community has been actively evaluating its performance for 3D medical image segmentation. However, different stu…
Supercharging Federated Learning with Flower and NVIDIA FLARE
Holger R. Roth, Daniel J. Beutel, Yan Cheng +13
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicate…
HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization
Yucheng Tang, Yufan He, Vishwesh Nath +11
In digital pathology, the traditional method for deep learning-based image segmentation typically involves a two-stage process: initially segmenting high-resolution whole slide ima…
VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
Yufan He, Pengfei Guo, Yucheng Tang +11
Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the…
Empowering Federated Learning for Massive Models with NVIDIA FLARE
Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh +12
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most stat…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Aorta Segmentation from 3D CT in MICCAI SEG.A. 2023 Challenge
Andriy Myronenko, Dong Yang, Yufan He +1
Aorta provides the main blood supply of the body. Screening of aorta with imaging helps for early aortic disease detection and monitoring. In this work, we describe our solution to…
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge
Andriy Myronenko, Dong Yang, Yufan He +1
Kidney and Kidney Tumor Segmentation Challenge (KiTS) 2023 offers a platform for researchers to compare their solutions to segmentation from 3D CT. In this work, we describe our su…
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
Jingwei Sun, Ziyue Xu, Hongxu Yin +4
Pre-trained language models (PLM) have revolutionized the NLP landscape, achieving stellar performances across diverse tasks. These models, while benefiting from vast training data…
Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training
Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang +8
Harnessing the power of pre-training on large-scale datasets like ImageNet forms a fundamental building block for the progress of representation learning-driven solutions in comput…
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle +17
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be te…
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Jingwei Sun, Ziyue Xu, Dong Yang +6
Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) dea…
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Meirui Jiang, Holger R Roth, Wenqi Li +6
How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness)…
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
Chun-Yin Huang, Ruinan Jin, Can Zhao +2
While Federated Learning (FL) is gaining popularity for training machine learning models in a decentralized fashion, numerous challenges persist, such as asynchronization, computat…
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
Chulin Xie, De-An Huang, Wenda Chu +4
Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Holger R. Roth, Yan Cheng, Yuhong Wen +20
Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVI…
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Holger R. Roth, Ali Hatamizadeh, Ziyue Xu +4
Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be…
Automated head and neck tumor segmentation from 3D PET/CT
Andriy Myronenko, Md Mahfuzur Rahman Siddiquee, Dong Yang +2
Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET…
Automated segmentation of intracranial hemorrhages from 3D CT
Md Mahfuzur Rahman Siddiquee, Dong Yang, Yufan He +2
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a platform for researchers to compare their solutions to segmentation of hemorrhage stroke regions from 3D CTs…