0. Referencehttps://arxiv.org/abs/1503.08895 End-To-End Memory NetworksWe introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires signifiarxiv.org1. Introduction- 현재 연구에서 포커싱하고 있는 도전 과제는 두 가지이다.- 첫 번째 : QA 문제..
0. Referencehttps://arxiv.org/abs/2212.06515 AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesThe survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restarx..
0. Referencehttps://link.springer.com/article/10.1186/s12920-020-0686-1#availability-of-data-and-materials0.1. Data sethttps://www.cancer.gov/tcga --> TCGA Research Network에서 생성된 데이터를 기반으로 논문이 작성됨 The Cancer Genome Atlas Program (TCGA)The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that sequenced and molecularly characterized over 11,000 cases of primary cancer samples. Lear..
0. Referencehttps://www.nature.com/articles/s41598-022-16283-30.1. Data-set(대장암 및 위암 환자의 조직학적 이미지의 패치 411,890개)https://zenodo.org/records/2530835 Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samplesThis repository contains 411,890 unique image patches derived from histological images of colorectal cancer and gastric cancer patients in the TCGA cohort (origi..
