1.5k citations · 2.9k across the 16 of their papers we have counts for
10 papers · 1 filter
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…
BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases
Qi Chen, Wenxuan Li, Pedro R. A. S. Bassi +14
Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsistently across real-world clinic…
RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
Wenxuan Li, Pedro R. A. S. Bassi, Xinze Zhou +3
Cancer screening is a reasoning task. A radiologist observes findings, compares them to prior scans, integrates clinical context, and reaches a diagnostic conclusion confirmed by p…
Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI
Ashraya K. Indrakanti, Jakob Wasserthal, Martin Segeroth +6
Purpose: To develop an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI, and compare models trai…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Combined tract segmentation and orientation mapping for bundle-specific tractography
Jakob Wasserthal, Peter Neher, Dusan Hirjak +1
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms…
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Fabian Isensee, Jens Petersen, Andre Klein +8
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation…
Tract orientation mapping for bundle-specific tractography
Jakob Wasserthal, Peter F. Neher, Klaus H. Maier-Hein
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms…
TractSeg - Fast and accurate white matter tract segmentation
Jakob Wasserthal, Peter Neher, Klaus H. Maier-Hein
The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI…
Direct White Matter Bundle Segmentation using Stacked U-Nets
Jakob Wasserthal, Peter F. Neher, Fabian Isensee +1
The state-of-the-art method for automatically segmenting white matter bundles in diffusion-weighted MRI is tractography in conjunction with streamline cluster selection. This proce…