Kyunghyun Paeng

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

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 a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen’s kappa in mitosis counting-based score prediction and 0.652 F1-score in mitosis detection. On Spearman’s correlation coefficient, which evaluates predictive accuracy on the molecular data based score, the system obtained 0.6171. Our approach won first place in all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which is MICCAI grand challenge.

Bi-DCT: DCT-based Local Binary Descriptor for Dense Stereo Matching

In this letter, we present a novel DCT-based local binary descriptor for the dense matching of multiple view stereo (MVS). Recently, although much progress has been made in the field of MVS, a key component of which, i.e., dense matching, is still a challenging task because it has two difficult issues: robust matching over non-salient regions (e.g., lines and textureless regions) and fast matching of a large number of pixels. To deal with these issues effectively, in the proposed dense descriptor, 2D DCT-based local features are utilized to achieve high discriminative power even for the non-salient regions. A binary representation is adopted to increase the matching performance as well as accelerate the matching speed via the Hamming distance. In addition, the discriminability of binarized vectors is further improved by a space-frequency pooling scheme. Through extensive experiments on the benchmark datasets for MVS, we demonstrate the superiority of the proposed descriptor over the state-of-the-art descriptors in terms of accuracy and efficiency.

Image-based object reconstruction using run-length representation

This paper presents an image-based object reconstruction with a low memory footprint using run-length representation. While conventional volume-based approaches, which utilize voxels as primitives, are intuitive and easy to manipulate 3D data, they require a large amount of memory and computation during the reconstruction process. To overcome these burdens, this paper uses 3D runs to represent a 3D object and reconstructs each 3D run from multi-view silhouettes with a small amount of memory. The proposed geometry reconstruction is also computationally inexpensive, as it processes multiple voxels simultaneously. And for the compatibility with the conventional data formats, generation of polygonal 3D meshes from the reconstructed 3D runs is proposed as well. Lastly, texture mapping is proposed to additionally reduce the amount of memory for object reconstruction. The proposed reconstruction scheme has been simulated using various types of multi-view datasets. The results show that the proposed method performs object reconstruction with a smaller amount of memory and computation than voxel-based approaches. An image-based object reconstruction using run-length representation is proposed.A fast geometry reconstruction by rectifying images is proposed.A 3D mesh generation algorithm from the reconstructed 3D runs is proposed.View dependent texture mapping algorithm using a color palette is proposed.

A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer.

Predicting TNM stage is the major determinant of breast cancer prognosis and treatment. The essential part of TNM stage classification is whether the cancer has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage (pN-stage) is commonly performed by pathologists detecting metastasis in histological slides. However, this diagnostic procedure is prone to misinterpretation and would normally require extensive time by pathologists because of the sheer volume of data that needs a thorough review. Automated detection of lymph node metastasis and pN-stage prediction has a great potential to reduce their workload and help the pathologist. Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images. In this paper, we propose a robust method for metastasis detection and pN-stage classification in breast cancer from multiple gigapixel pathology images in an effective way. pN-stage is predicted by combining patch-level CNN based metastasis detector and slide-level lymph node classifier. The proposed framework achieves a state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17 dataset, outperforming the previous winning method of the Camelyon17 challenge.

PRISM: a system for weighted multi-color browsing of fashion products

Multiple color search technology helps users find fashion products in a more intuitive manner. Although fashion product images can be represented not only by a set of dominant colors but also by the relative ratio of colors, current online fashion shopping malls often provide rather simple color filters. In this demo, we present PRISM (Perceptual Representation of Image SiMilarity), a weighted multi-color browsing system for fashion products retrieval. Our system combines widely accepted backend web service stacks and various computer vision techniques including a product area parsing and a compact yet effective multi-color description. Finally, we demonstrate the benefits of PRISM system via web service in which users freely browse fashion products.