Computer Vision134 [2025-1] 전연주 - Multi‑modal transformer architecture for medical image analysis and automated report generation 논문 링크: https://www.nature.com/articles/s41598-024-69981-5.pdf저자: Santhosh Raminedi, S. Shridevi & Daehan Won학회: Scientific Reports (Nature Publishing Group)발행일: 20 August 2024 Keywords: Vision transformer, Generative pre-trained transformer, Retrieval augmentationIntroduction1. 배경X-ray 및 MRI(Magnetic Resonance Imaging)와 같은 의료 영상 기술은 인체 내부를 시각화하여 질병을 진단하는 데 중요한 역할을 한다.그러나 방사선 영상(radiological ima.. 2025. 3. 15. [2025-1] 임수연 - MobileUNETR https://arxiv.org/abs/2409.03062 MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image SegmentationSkin cancer segmentation poses a significant challenge in medical image analysis. Numerous existing solutions, predominantly CNN-based, face issues related to a lack of global contextual understanding. Alternatively, some approaches resort to large-scale Tarxiv.. 2025. 3. 14. [2025-1] 유경석 - XprospeCT: CT Volume Generation from Paired X-Rays https://arxiv.org/abs/2403.00771 XProspeCT: CT Volume Generation from Paired X-RaysComputed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray imaging. In this paarxiv.org AbstractComputed tomography (CT) : 해부학적 구조를 반영한 d.. 2025. 3. 14. [2025-1] 황징아이 - Dynamic Routing Between Capsules NIPS 2017년 논문저자 : Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton논문 : https://arxiv.org/pdf/1710.09829 2017년 Google Brain의 Geoffrey E. Hinton이 발표한 Object Recognition 분야의 새로운 접근방법을 제안한 논문이다. 기존의 Object Recognition모델들은 Convolution Network를 기반으로 설계되어있다. Convolution Network는 feature extracting - max pooling 로 이루어져있다. 여기서 maxpooling의 정보손실 문제를 지적하며 CapsuleNet를 소개했다. 1. 개념이해Invariance vs. Equivarianc.. 2025. 3. 8. 이전 1 ··· 6 7 8 9 10 11 12 ··· 34 다음
