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$Multifractal Recalibration of Neural Networks for Medical Imaging Segmentation$

Multifractal Recalibration of Neural Networks for Medical Imaging Segmentation

본 논문은 다중프랙탈 이론을 딥러닝 기반 의미분할 모델에 통합하는 새로운 패러다임을 제시한다. 기존의 다중프랙탈 스펙트럼(MFS) 분석은 주로 이미지 텍스처의 복잡성을 정량화하거나, 비지도 학습에서 특징 추출에 활용돼 왔으며, 그 과정에서 고차 통계량을 계산하기 위해 대규모 풀링이나 차원 축소가 필수적이었다. 이러한 연산은 메모리와 시간 측면에서 비효율적이며, 특히 픽셀‑단위 예측이 요구되는 의미분할 작업에서는 실용성이 떨어진다. 저자들은 이 문제를 해결하기 위해 ‘단일프랙탈 재보정(Monofractal Recalibration)’

Multifractal Recalibration of Neural Networks for Medical Imaging Segmentation

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