38 citations · 88 across the 64 of their papers we have counts for
24 papers · 1 filter
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…
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Sucheng Ren, Qihang Yu, Ju He +3
Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through…
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
Sucheng Ren, Yaodong Yu, Nataniel Ruiz +3
There exists recent work in computer vision, named VAR, that proposes a new autoregressive paradigm for image generation. Diverging from the vanilla next-token prediction, VAR stru…
Label Critic: Design Data Before Models
Pedro R. A. S. Bassi, Qilong Wu, Wenxuan Li +4
As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiol…
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…
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
Feng Wang, Timing Yang, Yaodong Yu +7
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representation…
Analyzing Tumors by Synthesis
Qi Chen, Yuxiang Lai, Xiaoxi Chen +3
Computer-aided tumor detection has shown great potential in enhancing the interpretation of over 80 million CT scans performed annually in the United States. However, challenges ar…
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation
Yunfei Xie, Cihang Xie, Alan Yuille +1
In this paper, we introduce a hierarchical transformer-based model designed for sophisticated image segmentation tasks, effectively bridging the granularity of part segmentation wi…
Embracing Massive Medical Data
Yu-Cheng Chou, Zongwei Zhou, Alan Yuille
As massive medical data become available with an increasing number of scans, expanding classes, and varying sources, prevalent training paradigms -- where AI is trained with multip…
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
Wenxuan Li, Chongyu Qu, Xiaoxi Chen +18
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and f…
Autoregressive Pretraining with Mamba in Vision
Sucheng Ren, Xianhang Li, Haoqin Tu +9
The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capab…
Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
Sucheng Ren, Xiaoke Huang, Xianhang Li +5
This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentati…
Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasets
Yixiong Chen, Zongwei Zhou, Alan Yuille
An increasing number of public datasets have shown a transformative impact on automated medical segmentation. However, these datasets are often with varying label quality, ranging…
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
Jie Liu, Yixiao Zhang, Kang Wang +8
The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detail…
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning
Sucheng Ren, Hongru Zhu, Chen Wei +3
This paper presents a new self-supervised video representation learning framework, ARVideo, which autoregressively predicts the next video token in a tailored sequence order. Two k…
Mamba-R: Vision Mamba ALSO Needs Registers
Feng Wang, Jiahao Wang, Sucheng Ren +6
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in…
ViTamin: Designing Scalable Vision Models in the Vision-Language Era
Jieneng Chen, Qihang Yu, Xiaohui Shen +2
Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to thos…
Towards Generalizable Tumor Synthesis
Qi Chen, Xiaoxi Chen, Haorui Song +4
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor…
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
Tiange Xiang, Yixiao Zhang, Yongyi Lu +4
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploit…
From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai, Xiaoxi Chen, Angtian Wang +2
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in…
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
Yuanhao Cai, Yixun Liang, Jiahao Wang +5
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on N…
Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?
Tiezheng Zhang, Xiaoxi Chen, Chongyu Qu +2
Interactive segmentation, an integration of AI algorithms and human expertise, premises to improve the accuracy and efficiency of curating large-scale, detailed-annotated datasets…
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter
Junfei Xiao, Zheng Xu, Alan Yuille +2
This paper demonstrates that a progressively aligned language model can effectively bridge frozen vision encoders and large language models (LLMs). While the fundamental architectu…
SPFormer: Enhancing Vision Transformer with Superpixel Representation
Jieru Mei, Liang-Chieh Chen, Alan Yuille +1
In this work, we introduce SPFormer, a novel Vision Transformer enhanced by superpixel representation. Addressing the limitations of traditional Vision Transformers' fixed-size, no…