activity
20202022
most citedLearn molecular representations from large-scale unlabeled molecules for drug discovery

24 citations · 45 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CL2022

SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models

Changyu Hou, Jun Wang, Yixuan Qiao +8

Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play…

cs.CL2022

A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation

Tianxiang Sun, Xiangyang Liu, Wei Zhu +7

Early exiting allows instances to exit at different layers according to the estimation of difficulty. Previous works usually adopt heuristic metrics such as the entropy of internal…

cs.CV20211 cited

Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT

Xianghua Ye, Dazhou Guo, Chen-kan Tseng +22

Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To…

eess.IV20213 cited

DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search

Dazhou Guo, Xianghua Ye, Jia Ge +9

Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists a…

eess.IV2021

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

Youbao Tang, Ke Yan, Jinzheng Cai +6

Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly…

eess.IV2021

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss

Youbao Tang, Jinzheng Cai, Ke Yan +6

Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as univer…