331 citations · 390 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
Sangheum Hwang, Sunggyun Park
We introduce an accurate lung segmentation model for chest radiographs based on deep convolutional neural networks. Our model is based on atrous convolutional layers to increase th…
Action-Driven Object Detection with Top-Down Visual Attentions
Donggeun Yoo, Sunggyun Park, Kyunghyun Paeng +2
A dominant paradigm for deep learning based object detection relies on a "bottom-up" approach using "passive" scoring of class agnostic proposals. These approaches are efficient bu…
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…
Pixel-Level Domain Transfer
Donggeun Yoo, Namil Kim, Sunggyun Park +2
We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To…
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,…
Fisher Kernel for Deep Neural Activations
Donggeun Yoo, Sunggyun Park, Joon-Young Lee +1
Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but p…