This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021.
Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains; and (2) can suffer from feature space over collapse which can threaten generalization performance.
Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.
no code implementations • 29 Apr 2021 • Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang, ChiHung Lin, Le Lu, Lingyun Huang, Mei Han, Guotong Xie, Jing Xiao, Kuo Chang-Fu, Adam Harrison, Shun Miao
It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability.
In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task.
Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention.
Ranked #4 on Medical Image Segmentation on Synapse multi-organ CT
Given a chest X-ray image in the testing phase, if it is normal, the learned architecture can well model and reconstruct the content; if it is abnormal, since the content is unseen in the training phase, the model would perform poorly in its reconstruction.
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.