Models, code, and papers for "Byungjae Lee":
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.
Among underwater perceptual sensors, imaging sonar has been highlighted for its perceptual robustness underwater. The major challenge of imaging sonar, however, arises from the difficulty in defining visual features despite limited resolution and high noise levels. Recent developments in deep learning provide a powerful solution for computer-vision researches using optical images. Unfortunately, deep learning-based approaches are not well established for imaging sonars, mainly due to the scant data in the training phase. Unlike the abundant publically available terrestrial images, obtaining underwater images is often costly, and securing enough underwater images for training is not straightforward. To tackle this issue, this paper presents a solution to this field's lack of data by introducing a novel end-to-end image-synthesizing method in the training image preparation phase. The proposed method present image synthesizing scheme to the images captured by an underwater simulator. Our synthetic images are based on the sonar imaging models and noisy characteristics to represent the real data obtained from the sea. We validate the proposed scheme by training using a simulator and by testing the simulated images with real underwater sonar images obtained from a water tank and the sea.
This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state-of-the-art video tracking techniques shows very encouraging results.