Deep Learning30 [ResNet] He et al., 2015, Deep Residual Learning for Image Recognition # 세줄 요약 # We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth (evaluate residual nets with a depth of up to 152 layers). This result won the 1st place on the.. 2021. 10. 17. 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. Zhang et al., 2019, An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets # 세줄 요약 # Cancer has been one of the most threatening diseases, so our major goal is to identify malignant from benign lesions at radiology or CT imaging in the early stages, But it is difficult to collect such a large volume of images with pathological information. This paper explores two CNN models by focusing extensively on the expansion of training samples from two small pathologically prove.. 2021. 9. 27. Medina et al., 2020. Deep learning method for segmentation of rotator cuff muscles on MR images # 세줄 요약 # To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. For model A, we manually selected shoulder sagittal T1 Y-view from 258 cases as ground truth to train a classification, For model B, we manually segmented subscapularis, s.. 2021. 9. 19. Shim et al., 2020, Automated rotator cuff tear classification using 3D convolutional neural network # 세줄 요약 # Rotator cuff tear (RCT) is one of the most common shoulder injuries. When diagnosing RCT, skilled orthopedists visually interpret magnetic resonance imaging (MRI) scan data. MRI data from 2,124 patients were used to train and test the VRN-based 3D CNN to classify RCT into five classes (None, Partial, Small, Medium, Large-to-Massive). The VRN-based 3D CNN outperformed orthopedists speci.. 2021. 8. 8. Kanavati et al., 2021, A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images # 세줄 요약 # The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLS) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis, but if this diag.. 2021. 7. 18. Lindsay et al., 2018, Transfer Learning Approach to Predict Biopsy-Confirmed Malignancy of Lung Nodules from Imaging Data:A Pilot Study # 세줄요약 # Dataset Includes 796 patient have pathology-confirmed diagnosis(from CT-guided biopsy) and high-resolution CT imaging data at one institution between 2012 and 2017. To avoid overfitting on small dataset, Transfer learning to train a network using open dataset(LIDC) and added three new untrained layers. These study using only 86 patients, because Lesion location was manually determined u.. 2020. 10. 5. Choi & Jin, 2018, Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging # 세줄요약 # 인간에게 치명적인 인지기능 장애를 가져오는 알츠하이머 질병을 진단하기 위하여 CNN 기반의 진단 알고리즘을 개발하였으며, 먼저 AD(Alzheimer Disease)와 NC(Normal Control)을 분류하는 모델을 먼저 학습시킨 후, MCI(Mild Cognitive Inpairment) 환자들이 치매로 전환(Converter or Nonconverter)되는지를 학습시켰다. 학습 데이터로는 ADNI 오픈 데이터셋의 FDG와 AV-45 PET 영상들을 사용하였으며, 이때 딥러닝은 특성 추출을 자동으로 하기에 Spatial Normalization 같이 뇌영상에서 전통적으로 사용되던 영상처리 기법들은 적용하지 않았다. 학습 결과 AD vs NC 환자 분류와 MCI 환자들 중 AD 환자.. 2020. 2. 24. Ardila et al., 2019, End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography # 세줄요약 # We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies: Fi.. 2019. 11. 11. Jo et al., 2019, Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. # 세줄요약 # 최근 뇌영상 처리(Neuroimaging Techniques) 기법들의 빠른 발전과 MRI, PET 등 다양한 종류에서 방대한 양의 뇌영상 데이터들이 나오면서, 딥러닝을 이용하여 알츠하이머 질병의 조기 발견과 자동 분류를 해주는 진단 모델들이 연구 및 개발되고 있다. 이 논문에서는 그러한 알츠하이머 진단 모델들의 기존 연구들을 평가하고 정리하기 위해 총 16개의 논문들을 리뷰하였으며, 그 중 4개는 딥러닝과 함께 전통적인 머신러닝 기법을 사용한 연구들이고, 나머지 12개는 오직 딥러닝 기법만 사용한 연구들이다. 딥러닝 기법은 기존의 전통적인 머신러닝 기법에서 전제되는 특성 추출을 위한 복잡하고 어려운 뇌 영상 처리 기법들을 적용할 필요가 없기에, 현재 다양한 종류의 뇌영상(MRI, PET.. 2019. 10. 15. 이전 1 2 3 다음
