336 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Korea Advanced Institute of Science and TechnologyKR2 papers
- Adobe Systems (United States)US1 paper
- Beihang UniversityCN1 paper
- Beth Israel Deaconess Medical CenterUS1 paper
- Eindhoven University of TechnologyNL1 paper
- German Cancer Research CenterDE1 paper
- Hanbat National UniversityKR1 paper
- Harvard UniversityUS1 paper
- Heidelberg UniversityDE1 paper
- IBM (Brazil)BR1 paper
- IBM Research - BrazilBR1 paper
- IBM Research - ZurichCH1 paper
6 papers · 1 filter
Exploiting Features with Split-and-Share Module
Jaemin Lee, Minseok Seo, Jongchan Park +1
Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing…
CBAM: Convolutional Block Attention Module
Sanghyun Woo, Jongchan Park, Joon-Young Lee +1
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, o…
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Mitko Veta, Yujing J. Heng, Nikolas Stathonikos +30
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective an…
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
Cesc Chunseong Park, Byeongchang Kim, Gunhee Kim
We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, account…
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park +1
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both…
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Donggeun Yoo, Sunggyun Park, Joon-Young Lee +2
We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet. We cast an object detection problem as an iterative classification problem,…