38 citations · 88 across the 64 of their papers we have counts for
70 papers
DART: Scaling a Shape Representation Across Anatomical Classes and Imaging Modalities
Koushik Swarna, Pedro R. A. S. Bassi, Lam Ngo +4
Current state-of-the-art segmentation models still produce anatomically implausible shapes, such as broken vessels and fragmented glands, on the structures that are hardest to segm…
Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset
Pedro R. A. S. Bassi, Wenxuan Li, Szymon Plotka +10
Multi-cancer segmentation in computed tomography (CT) is fundamentally limited by the scarcity of tumor masks across different organs. We present Merlin Plus, the first large-scale…
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Pedro R. A. S. Bassi, Wenxuan Li, Hanxue Gu +11
Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are…
Report Supervision
Pedro R. A. S. Bassia, Wenxuan Li, Jakob Wasserthal +10
Segmentation models can surpass radiologists, classification models, and vision-language models in tumor detection. Importantly, segmentation models outline tumors, allowing radiol…
Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
Qi Chen, Shuhan Ding, Yu Gu +5
Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from scr…
Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling
Junqi Liu, Xinze Zhou, Wenxuan Li +10
Photon-counting CT (PCCT) provides superior image quality with higher spatial resolution and lower noise compared to conventional energy-integrating CT (EICT), but its limited clin…
ViT-5: Vision Transformers for The Mid-2020s
Feng Wang, Sucheng Ren, Tiezheng Zhang +4
This work presents a systematic investigation into modernizing Vision Transformer backbones by leveraging architectural advancements from the past five years. While preserving the…
WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark
Wang Lin, Feng Wang, Majun Zhang +7
Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synth…
CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs
Haoran Li, Sucheng Ren, Alan Yuille +1
Rotary Positional Embedding (RoPE) is a key component of context scaling in Large Language Models (LLMs). While various methods have been proposed to adapt RoPE to longer contexts,…
Spiral RoPE: Rotate Your Rotary Positional Embeddings in the 2D Plane
Haoyu Liu, Sucheng Ren, Tingyu Zhu +5
Rotary Position Embedding (RoPE) is the de facto positional encoding in large language models due to its ability to encode relative positions and support length extrapolation. When…
Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomography
Wenxuan Li, Pedro R. A. S. Bassi, Lizhou Wu +34
Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective reviews of prediagnostic CT scans,…
Large-Scale Label Quality Assessment for Medical Segmentation via a Vision-Language Judge and Synthetic Data
Yixiong Chen, Zongwei Zhou, Wenxuan Li +1
Large-scale medical segmentation datasets often combine manual and pseudo-labels of uneven quality, which can compromise training and evaluation. Low-quality labels may hamper perf…
Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges
Ariel Lubonja, Pedro R. A. S. Bassi, Wenxuan Li +4
Open challenges have become the de facto standard for comparative ranking of medical AI methods. Despite their importance, medical AI leaderboards exhibit three persistent limitati…
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
Junqi Liu, Zejun Wu, Pedro R. A. S. Bassi +15
Image enhancement improves visual quality and helps reveal details that are hard to see in the original image. In medical imaging, it can support clinical decision-making, but curr…
ViMix-14M: A Curated Multi-Source Video-Text Dataset with Long-Form, High-Quality Captions and Crawl-Free Access
Timing Yang, Sucheng Ren, Alan Yuille +1
Text-to-video generation has surged in interest since Sora, yet open-source models still face a data bottleneck: there is no large, high-quality, easily obtainable video-text corpu…
RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models
Timing Yang, Guoyizhe Wei, Alan Yuille +1
Mamba has recently garnered attention as an effective backbone for vision tasks. However, its underlying mechanism in visual domains remains poorly understood. In this work, we sys…
Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data
Qi Chen, Xinze Zhou, Chen Liu +11
AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,…
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
Pedro R. A. S. Bassi, Xinze Zhou, Wenxuan Li +20
Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screenin…
4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos
Shanshan Zhong, Jiawei Peng, Zehan Zheng +6
Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is lab…
Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models
Tiezheng Zhang, Yitong Li, Yu-cheng Chou +4
Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
Learning Segmentation from Radiology Reports
Pedro R. A. S. Bassi, Wenxuan Li, Jieneng Chen +8
Tumor segmentation in CT scans is key for diagnosis, surgery, and prognosis, yet segmentation masks are scarce because their creation requires time and expertise. Public abdominal…
PanTS: The Pancreatic Tumor Segmentation Dataset
Wenxuan Li, Xinze Zhou, Qi Chen +15
PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validat…
ShapeKit
Junqi Liu, Dongli He, Wenxuan Li +3
In this paper, we present a practical approach to improve anatomical shape accuracy in whole-body medical segmentation. Our analysis shows that a shape-focused toolkit can enhance…