나만의 길

https://arxiv.org/abs/2403.09257 WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide imagesThe Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs are not suitable for the multi-scale nature of whole-slide images (WSIs), resarxiv.orgOverview..

https://arxiv.org/abs/2304.12306 Segment Anything in Medical Images Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, current methods predominantly rely on customized models, which exhibit limited generality across arxiv.org https://github.com/bowang-lab/MedSAM GitHub - bowang-lab/MedSAM:..

https://arxiv.org/abs/2004.05024 Weakly supervised multiple instance learning histopathological tumor segmentation Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology arxiv.org 해당 논문은 MICCAI 202..

https://arxiv.org/abs/1803.10464 Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels. In this wea..

https://www.frontiersin.org/articles/10.3389/fmed.2019.00264/full Deep Learning for Whole Slide Image Analysis: An Overview The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual un www.frontiersin.o..

본 내용은 밑바닥부터 시작하는 딥러닝 1을 바탕으로 학습한 내용을 정리했습니다. https://github.com/WegraLee/deep-learning-from-scratch 1. Activation function layer 구현하기 앞서 구현한 계산 그래프를 신경망에 적용해보겠습니다. 우선 Activation function인 ReLU와 Sigmoid 계층을 구현하겠습니다. 1.1 ReLU ReLU의 수식은 다음과 같습니다. 해당 수식에 대한 미분값은 다음과 같습니다. 해당 함수는 순전파 때 \(x\)가 0보다 크면 역전파는 상류의 값을 다음 노드로 전달합니다. 반면 0 이하라면 전달하지 않게 됩니다. 계산 그래프로 다음과 같이 표현할 수 있습니다. ReLU함수의 forward(), backwar..