Sang Jun Lee

Juha Park, Jiho Choi, Jong Pil Yun et al.

Accurate segmentation of maxillary sinus in panoramic X-ray images is essential for dental diagnosis and surgical planning; however, this task remains relatively underexplored in dental imaging research. Structural overlap, ambiguous anatomical boundaries inherent to two-dimensional panoramic projections, and the limited availability of large scale clinical datasets with reliable pixel-level annotations make the development and evaluation of segmentation models challenging. To address these challenges, we propose a semi-supervised segmentation framework that effectively leverages both labeled and unlabeled panoramic radiographs, where knowledge distillation is utilized to train a student model with reliable structural information distilled from a teacher model. Specifically, we introduce a weighted knowledge distillation loss to suppress unreliable distillation signals caused by structural discrepancies between teacher and student predictions. To further enhance the quality of pseudo labels generated by the teacher network, we introduce SinusCycle-GAN which is a refinement network based on unpaired image-to-image translation. This refinement process improves the precision of boundaries and reduces noise propagation when learning from unlabeled data during semi-supervised training. To evaluate the proposed method, we collected clinical panoramic X-ray images from 2,511 patients, and experimental results demonstrate that the proposed method outperforms state-of-the-art segmentation models, achieving the Dice score of 96.35\% while reducing boundary error. The results indicate that the proposed semi-supervised framework provides robust and anatomically consistent segmentation performance under limited labeled data conditions, highlighting its potential for broader dental image analysis applications.

Jiho Choi, Sang Jun Lee

In this paper, we propose a method that learns a general representation of periodic signals from unlabeled facial videos by capturing subtle changes in skin tone over time. The proposed framework employs the video masked autoencoder to learn a high-dimensional spatio-temporal representation of the facial region through self-supervised learning. Capturing quasi-periodic signals in the video is crucial for remote photoplethysmography (rPPG) estimation. To account for signal periodicity, we apply frame masking in terms of video sampling, which allows the model to capture resampled quasi-periodic signals during the pre-training stage. Moreover, the framework incorporates physiological bandlimit constraints, leveraging the property that physiological signals are sparse within their frequency bandwidth to provide pulse cues to the model. The pre-trained encoder is then transferred to the rPPG task, where it is used to extract physiological signals from facial videos. We evaluate the proposed method through extensive experiments on the PURE, UBFC-rPPG, MMPD, and V4V datasets. Our results demonstrate significant performance improvements, particularly in challenging cross-dataset evaluations. Our code is available at https://github.com/ziiho08/Periodic-MAE.

Gyutae Hwang, Sang Jun Lee

Human health can be critically affected by cardiovascular diseases, such as hypertension, arrhythmias, and stroke. Heart rate and blood pressure are important biometric information for the monitoring of cardiovascular system and early diagnosis of cardiovascular diseases. Existing methods for estimating the heart rate are based on electrocardiography and photoplethyomography, which require contacting the sensor to the skin surface. Moreover, catheter and cuff-based methods for measuring blood pressure cause inconvenience and have limited applicability. Therefore, in this thesis, we propose a vision-based method for estimating the heart rate and blood pressure. This thesis proposes a 2-stage deep learning framework consisting of a dual remote photoplethysmography network (DRP-Net) and bounded blood pressure network (BBP-Net). In the first stage, DRP-Net infers remote photoplethysmography (rPPG) signals for the acral and facial regions, and these phase-shifted rPPG signals are utilized to estimate the heart rate. In the second stage, BBP-Net integrates temporal features and analyzes phase discrepancy between the acral and facial rPPG signals to estimate SBP and DBP values. To improve the accuracy of estimating the heart rate, we employed a data augmentation method based on a frame interpolation model. Moreover, we designed BBP-Net to infer blood pressure within a predefined range by incorporating a scaled sigmoid function. Our method resulted in estimating the heart rate with the mean absolute error (MAE) of 1.78 BPM, reducing the MAE by 34.31 % compared to the recent method, on the MMSE-HR dataset. The MAE for estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 10.19 mmHg and 7.09 mmHg. On the V4V dataset, the MAE for the heart rate, SBP, and DBP were 3.83 BPM, 13.64 mmHg, and 9.4 mmHg, respectively.

Sang Jun Lee, Sang Woo Kim, Wookyong Kwon et al.

This paper proposes an algorithm for recognizing slab identification numbers in factory scenes. In the development of a deep-learning based system, manual labeling to make ground truth data (GTD) is an important but expensive task. Furthermore, the quality of GTD is closely related to the performance of a supervised learning algorithm. To reduce manual work in the labeling process, we generated weakly annotated GTD by marking only character centroids. Whereas bounding-boxes for characters require at least a drag-and-drop operation or two clicks to annotate a character location, the weakly annotated GTD requires a single click to record a character location. The main contribution of this paper is on selective distillation to improve the quality of the weakly annotated GTD. Because manual GTD are usually generated by many people, it may contain personal bias or human error. To address this problem, the information in manual GTD is integrated and refined by selective distillation. In the process of selective distillation, a fully convolutional network is trained using the weakly annotated GTD, and its prediction maps are selectively used to revise locations and boundaries of semantic regions of characters in the initial GTD. The modified GTD are used in the main training stage, and a post-processing is conducted to retrieve text information. Experiments were thoroughly conducted on actual industry data collected at a steelmaking factory to demonstrate the effectiveness of the proposed method.