논문 리뷰/의료영상28 [JR3] Vuno Published Journal Papers in Lung CT A.I. Journal Review in 3 lines: Vuno Published Journal Papers in Lung CT A.I. [Since 2023] 1. Park, Doohyun, et al. "Development and validation of a hybrid deep learning–machine learning approach for severity assessment of COVID-19 and other pneumonias." Scientific Reports 13.1 (2023): 13420. [Since 2022] 2. Park, Sohee, et al. "Application of computer-aided diagnosis for Lung-RADS categorization in .. 2024. 3. 30. [JIS] Deep Learning for Interstitial Lung Diseases (ILDs) / Idiopathic Pulmonary Fibrosis (IPF) Journal Introduction Summary: Deep Learning for Interstitial Lung Diseases (ILDs) / Idiopathic Pulmonary Fibrosis (IPF) [Since 2022] 1. Furukawa, Taiki, et al. "A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases." Respirology 27.9 (2022): 739-746. 간질성 폐질환 (Interstitial lung disease; 이하 ILD)은 다양한 예후와 그에 따른 .. 2024. 3. 1. [JR3] Deep Learning for Lung CT Slice Thickness Reduction Journal Review in 3 lines: Deep Learning for Lung CT Slice Thickness Reduction [Since 2020] 1. Hamabuchi, Nayu, et al. "Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images." Japanese journal of radiology 41.12 (2023): 1373-1388. 2. Wu, Shuqiong, et al. "Computed Tomography slice interpolation in the longitudinal direction based on deep lear.. 2023. 11. 24. Furukawa et al., 2022, A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases # 세줄 요약 # 특발성 폐 섬유증(Idiopathic pulmonary fibrosis; 이하 IPF)은 환자의 예후가 좋지 않으면서 다학제(multidisciplinary) 간의 진단 정확도 마저 낮기에, 이번 연구에서는 환자의 위험을 유발하는 수술이 배제된 비침습적(non-invasive) 데이터들을 활용하여 딥러닝과 머신러닝이 결합된 알고리즘으로, 보통의 간질성 폐질환(interstitial lung diseases; 이하 ILD)로부터 IPF 환자를 분류하였다. 이번 연구는 후향적(retrospectively) 연구로 2007년 4월부터 2017년 7월 사이에 ILD로 진단된 환자들의 데이터(총 1068명의 ILD 환자 중 42.7%가 IPF 진단)를 모았으며, 딥러닝은 HRCT 영상에서 병변을.. 2023. 8. 28. Handa et al., 2022, Novel Artificial Intelligence-based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis # 세줄 요약 # 연구 목적: 흉부 CT 영상에서 폐 조직(parenchymal)과 기도(airway)에서의 병변을 검출하는 인공지능 분석 소프트웨어를 개발하고, IPF(idiopathic pulmonary fibrosis; 특발성 폐 섬유증)를 가진 환자들의 예후를 예측 하는 것. 저자들은 고해상도 CT (high-resolution CT; HRCT) 304개를 가지고 자동으로 10가지 유형의 폐 조직 패턴(+기도)을 정량화하는 AIQCT(artificial intelligence-based quantitative CT image analysis software)를 개발하였고, 이후 개발된 AIQCT를 IPF를 가진 환자 120명의 HRCT 영상에 적용하여 병변의 볼륨과 생존률과의 관계를 분석하였다. .. 2023. 6. 9. Park et al., 2021, Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction # 세줄 요약 # 목적: CT section Thickness에 따라 CAD(Computer Aided Diagnosis)의 SSN(subsolid nodules) 검출 성능을 비교해보고, 딥러닝 기반의 super-resolution 기술을 적용하여 CT section Thickness를 변화시켰을 때 검출 성능이 향상되는지를 확인하는 것이 연구 목표이다. 방법: Lung CT 영상은 thick setction에 따라 각각 1, 3, 5mm 영상들을 모두 가진 환자들의 데이터를 모았으며(SSN은 6~30mm 이내의 결절들만 ground-truth로 레이블링), 각 thick setction에 따라 CAD로 검출을 시키고 추가로 3, 5mm thickness 영상들은 1mm thickness로 super-.. 2023. 5. 2. Goode et al., 2013, OpenSlide: A vendor-neutral software foundation for digital pathology. # 세줄 요약 # Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing, and interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library (easily extensible for various programming languages) for reading and manipulating digital slides o.. 2022. 2. 13. Wang et al., 2019, Pathology Image Analysis Using Segmentation Deep Learning Algorithms. # 세줄 요약 # With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Deep learning-based pathology image segmentation has become an important tool in WSI analysis because that algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. In t.. 2021. 12. 20. Hwang et al, 2019, Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. # 세줄 요약 # To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases (pulmonary malignant neoplasm, active tuberculosis, pneumonia, pneumothorax). This diagnostic study developed a deep learning-based algorithm using single-center data (chest radiographs: 54,221 normal findings; 35,613 abnormal findings) and extern.. 2021. 10. 20. Campanella et al., 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. # 세줄 요약 # The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. We present a multiple instance learning-based deep learning system that uses only reported diagnoses as labels for training. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph.. 2021. 10. 4. 이전 1 2 3 다음
