Improving the performance of object detection by preserving label distribution

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.Comment: Code is available at https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai

Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes

Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis

Effect of Residual Oxygen Concentration on the Lattice Parameters of Aluminum Nitride Powder Prepared via Carbothermal Reduction Nitridation Reaction

Residual oxygen in wurtzite-type aluminum nitride (AlN) crystal, which significantly affects phonon transport and crystal growth, is crucial to thermal conductivity and the crystal quality of AlN ceramics. In this study, the effect of residual oxygen on the lattice of AlN was examined for as-synthesized and sintered samples. By controlling reaction time in the carbothermal reduction nitridation (CRN) procedure, AlN powder was successfully synthesized, and the amount of residual oxygen was systematically controlled. The evolution of lattice parameters of AlN with respect to oxygen conc. was carefully investigated via X-ray diffraction analysis. With increasing amounts of residual oxygen in the as-synthesized AlN, lattice expansion in the ab plane was induced without a significant change in the c-axis lattice parameter. The lattice expansion in the ab plane owing to the residual oxygen was also confirmed with high-resolution transmission electron microscopy, in contrast to the invariant lattice parameter of the sintered AlN phase. Micro-strain values from XRD peak broadening confirm that stress, induced by residual oxygen, expands the AlN lattice. In this work, the lattice expansion of AlN with increasing residual oxygen was elucidated via X-ray diffraction and HR-TEM, which is useful to estimate and control the lattice oxygen in AlN ceramics

Clinical Validation of an Artificial Intelligence Model for Detecting Distal Radius, Ulnar Styloid, and Scaphoid Fractures on Conventional Wrist Radiographs

This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887–0.918), 0.925 (95% C.I. 0.911–0.939), and 0.808 (95% C.I. 0.748–0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66–0.83) to 0.85 (95% C.I. 0.77–0.93) and from 0.71 (95% C.I. 0.62–0.80) to 0.80 (95% C.I. 0.71–0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed

Direct Evidence on Effect of Oxygen Dissolution on Thermal and Electrical Conductivity of AlN Ceramics Using Al Solid-State NMR Analysis

Aluminum nitride, with its high thermal conductivity and insulating properties, is a promising candidate as a thermal dissipation material in optoelectronics and high-power logic devices. In this work, we have shown that the thermal conductivity and electrical resistivity of AlN ceramics are primarily governed by ionic defects created by oxygen dissolved in AlN grains, which are directly probed using 27Al NMR spectroscopy. We find that a 4-coordinated AlN3O defect (ON) in the AlN lattice is changed to intermediate AlNO3, and further to 6-coordinated AlO6 with decreasing oxygen concentration. As the aluminum vacancy (VAl) defect, which is detrimental to thermal conductivity, is removed, the overall thermal conductivity is improved from 120 to 160 W/mK because of the relatively minor effect of the AlO6 defect on thermal conductivity. With the same total oxygen content, as the AlN3O defect concentration decreases, thermal conductivity increases. The electrical resistivity of our AlN ceramics also increases with the removal of oxygen because the major ionic carrier is VAl. Our results show that to enhance the thermal conductivity and electrical resistivity of AlN ceramics, the dissolved oxygen in AlN grains should be removed first. This understanding of the local structure of Al-related defects enables us to design new thermal dissipation materials