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Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
Transoesophageal echocardiography (TEE) is a valuable diagnostic and mon...
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Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating
Lung Cancer is the most common cause of cancer-related death worldwide. ...
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Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms
Uterine cancer, also known as endometrial cancer, can seriously affect t...
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Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Accurate delineation of the intraprostatic gross tumour volume (GTV) is ...
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Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer
Developing nations lack adequate number of hospitals with modern equipme...
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Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images
Optical coherence tomography (OCT) can provide high-resolution cross-sec...
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SDCT-AuxNet^θ: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis
Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood ...
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Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network
The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer. The current standard inspection is evaluating Gleason H E-stained histopathology images by pathologists. However, it is complicated, time-consuming, and subject to observers. Deep learning (DL) based-methods that automatically learn image features and achieve higher generalization ability have attracted significant attention. However, challenges remain especially using DL to train the whole slide image (WSI), a predominant clinical source in the current diagnostic setting, containing billions of pixels, morphological heterogeneity, and artifacts. Hence, we proposed a convolutional neural network (CNN)-based automatic classification method for accurate grading of PCa using whole slide histopathology images. In this paper, a data augmentation method named Patch-Based Image Reconstruction (PBIR) was proposed to reduce the high resolution and increase the diversity of WSIs. In addition, a distribution correction (DC) module was developed to enhance the adaption of pretrained model to the target dataset by adjusting the data distribution. Besides, a Quadratic Weighted Mean Square Error (QWMSE) function was presented to reduce the misdiagnosis caused by equal Euclidean distances. Our experiments indicated the combination of PBIR, DC, and QWMSE function was necessary for achieving superior expert-level performance, leading to the best results (0.8885 quadratic-weighted kappa coefficient).
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