Deep learning-based analysis of brain magnetic resonance imaging (MRI) plays a crucial role in the early diagnosis of Alzheimer’s disease (AD). However, data scarcity and racial bias present significant challenges to the generalization of diagnostic models. Large-scale public datasets, which are predominantly composed of Caucasian individuals, often lead to performance degradation when applied to other ethnic groups owing to domain shifts. To address these issues, this study proposes a two-stage transfer learning framework. Initially, a 3D ResNet model was pretrained on a large-scale Alzheimer’s disease neuroimaging initiative (ADNI) dataset to learn structural brain features. Subsequently, the pretrained weights were transferred and fine-tuned on a small-scale Korean dataset utilizing only 30% of the data for training. The proposed model achieved superior performance in classifying mild cognitive impairment (MCI), which is crucial for early diagnosis, compared with a model trained from scratch using 70% of the Korean data. Furthermore, it effectively mitigated the significant performance degradation observed when directly applying the pretrained model, demonstrating its ability to resolve the domain-shift issue. This study explored the feasibility of transfer learning to address data scarcity and domain shift issues in AD classification, underscoring its potential for developing AI-based diagnostic systems tailored to specific ethnic populations.

최근 자율주행 기술의 빠른 발전에 따라 3D LiDAR 데이터를 활용한 동적 객체 분할(MOS, Moving Object Segmentation)의 정확성 확보가 자율주행 시스템에서 중요한 역할을 하고 있다. 자율주행 시스템은 주변 환경을 정확히 인식하고 객체의 위치와 움직임을 신속하게 판단해야 하므로, 동적 객체 분할의 정확도는 운전자 및 동승자의 안전과 직결된다. 그러나 기존 연구에서는 그림자와 같은 비동적 객체가 동적 객체로 잘못 인식되는 문제가 발생하며, 이는 자율주행 시스템의 오판으로 이어질 수 있다. 본 연구에서는 평면 탐지 기법과 프로젝션 거리 분석을 통해 3D LiDAR 환경에서 발생하는 그림자 영역과 같은 비동적 객체를 효과적으로 제거하는 방법을 제안한다. 제안된 방법은 자율주행 시스템이 주변 환경을 보다 신뢰성 있게 인식할 수 있도록 하여, 자율주행의 안전성과 안정성을 강화하는 데 기여할 것으로 기대된다.

Autonomous vehicles must recognize their surroundings and distinguish between dynamic and static objects to avoid collisions. Most recent moving object segmentation (MOS) studies project LiDAR point-cloud streams into multiple views to capture spatio-temporal cues. When a single view proves insufficient, the common strategy is to combine or convert views. However, existing methods rebuild features in full 3D space during conversion, significantly increasing time and memory costs. They also depend on high-end graphics processing units, which limits their on-board deployment. We introduce SwiftMOS, a lightweight framework centered on a direct view transformation module that rapidly converts bird's-eye view to range view without restoring 3D features. Direct view transformation leverages precomputed grids and view-specific hint maps to efficiently map coordinates, and an addition-fusion feature flow quickly merges past and current frames to capture the spatio-temporal context. Extensive quantitative and qualitative experiments on the SemanticKITTI dataset confirm the validity and real-time capability of the proposed method. SwiftMOS achieves a substantial latency reduction while maintaining competitive performance. It operates within the typical LiDAR scan rate on on-board hardware and also achieves robust performance on the Sipailou Campus Dataset, underscoring its practicality for real-world autonomous driving.