From the 3 of 5 linked papers with an AI index.
5 papers
Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge
Inhwa Son, Gaeun Lee, Sohyeon Sim +1
The paper presents a deep‑learning solution using nnU‑Net with adaptive intensity normalization confined to brain tissue to segment lesions in moderate to severe traumatic brain in…
MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification
Jiho Jun, Jeongwon Woo, Jaemin Song +6
The paper introduces MAGE, a framework that uses masked achromatic views and dual-objective knowledge distillation to train a model that classifies gastric adenoma versus carcinoma…
Decouple and Reason: Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT
Kwang-Hyun Uhm, Inhwa Son, Sung-Jea Ko
The paper introduces a two‑stage decoupled framework that first performs class‑agnostic lesion segmentation on 3D chest CT scans and then aligns the resulting sub‑volumes with free…
An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation
Kwang-Hyun Uhm, Hyunjun Cho, Sung-Hoo Hong +1
Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical diagnosis. In clinical practice, CT images are usually acquired with large slice…
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Muhammad Imran, Jonathan R. Krebs, Vishal Balaji Sivaraman +60
Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aort…