2 citations · 3 across the 8 of their papers we have counts for
9 papers
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…
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,…
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…
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…
RadGPT: Constructing 3D Image-Text Tumor Datasets
Pedro R. A. S. Bassi, Mehmet Can Yavuz, Kang Wang +7
Cancers identified in CT scans are usually accompanied by detailed radiology reports, but publicly available CT datasets often lack these essential reports. This absence limits the…
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…
Training with Explanations Alone: A New Paradigm to Prevent Shortcut Learning
Pedro R. A. S. Bassi, Haydr A. H. Ali, Andrea Cavalli +1
Application of Artificial Intelligence (AI) in critical domains, like the medical one, is often hampered by shortcut learning, which hinders AI generalization to diverse hospitals…
Faster ISNet for Background Bias Mitigation on Deep Neural Networks
Pedro R. A. S. Bassi, Sergio Decherchi, Andrea Cavalli
Bias or spurious correlations in image backgrounds can impact neural networks, causing shortcut learning (Clever Hans Effect) and hampering generalization to real-world data. ISNet…