Minho Lee

Incomplete Atypical Femoral Fracture (IAFF) is a precursor to Atypical Femoral Fracture (AFF). If untreated, it progresses to a complete fracture, increasing mortality risk. However, due to their small and ambiguous features, IAFFs are often misdiagnosed even by specialists. In this paper, we propose a novel approach for accurately classifying IAFFs in X-ray images across various radiographic views. We design a Dual Context-aware Complementary Extractor (DCCE) to capture both the overall femur characteristics and IAFF details with the surrounding context, minimizing information loss. We also develop a Level-wise Perspective-preserving Fusion Network (LPFN) that preserves the perspective of features while integrating them at different levels to enhance model representation and sensitivity by learning complex correlations and features that are difficult to obtain independently. Additionally, we incorporate the Spatial Anomaly Focus Enhancer (SAFE) to emphasize anomalous regions, preventing the model bias toward normal regions, and reducing False Negatives and missed IAFFs. Experimental results show significant improvements across all evaluation metrics, demonstrating high reliability in terms of accuracy (0.931), F1-score (0.9456), and AUROC (0.9692), proving the model’s potential for application in real medical settings.

Shreyanshu Bhushan, Eun-Soo Jung, Minho Lee. Findings of the Association for Computational Linguistics ACL 2024. 2024.

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes.

Air conditioner consumers expect their products to work well without any problem. Consumers encounter problems if the amount of refrigerant in the air conditioner is insufficient. Therefore, we propose a novel deep learning approach that predicts the amount of refrigerant in advance. Our approach differs from others, as it is not limited to specific types of air conditioners and is applicable to all types. We propose a domain adaptive Transformer (DAT) that determines the amount of refrigerant using a Transformer encoder and domain adversarial training of neural networks. The proposed DAT reduces the model parameters by using a modified lightweight Transformer encoder and adds a domain classifier to build a universal model that is not limited to a specific type of air conditioner. We conduct the experiments by collecting data on the amount of refrigerant operating in the real fields. As a result, the proposed DAT achieves higher prediction performance. It is also significant that the results predicted by DAT are more accurate than those predicted by experts dealing with air conditioner refrigerant problems. As a result, DAT-based air conditioners are commercialized in real life. Hence, consumers can prevent the shortage of refrigerant without experts.