Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

“Potentially, larger and deeper convolutional neural networks could be applied to further improve classification performance [59], [60].”

“In order to increase the variation of the training data and to avoid overfitting analogous to the data augmentation approach in [14], [17] and [18], multiple 2D or 2.”

“Potentially, larger and deeper convolutional neural networks could be applied to further improve classification performance [59], [60].”

“This type of cascade classification is similar in spirit to our presented second tier deep classifier (ConvNet), but uses state-of-the-art shallow classifiers (libSVM [47] and sparse linear fusion via the Relevance Vector Machine [48]).”

“Potentially allowing the ConvNet optimization to start from an initialization that is better than starting from Gaussian random parameters [57], [58].”

“Balancing between positive and negative training populations is generally beneficial for training ConvNets when optimizing with logistic regression cost [15], [14].”

“Likewise, the compositional two-stream 2D ConvNet models run on separate spatial (RGB) and temporal (i.e., optical flow field) video frames and achieve the mean accuracy of 87.9% in action classification task, based on a middle scale dataset UCF-101 [50].”

“It is a variation of the previously suggested “DropOut” method [29], [30].”

“This result significantly outperforms the direct 3D “spatial-temporal” ConvNet method [51] at 65.”

“Potentially allowing the ConvNet optimization to start from an initialization that is better than starting from Gaussian random parameters [57], [58].”

“As a practical compromise, 2.5D segmentation leverages spatial context through multi-view or stacked adjacent slices [60 ], effectively mitigating inter-slice artifacts while preserving computational efficiency.”

“This study focused on the initial and clinically crucial phase of the diagnostic workflow, specifically the automated localization of suspicious cervical lymph nodes on contrast-enhanced CT images (27).”

“The main challenge in applying deep learning to medical imaging is dealing with limited datasets and a shortage of annotated samples (Roth et al., 2016; Litjens et al., 2017; Greenspan et al., 2016; Tajbakhsh et al., 2016; Jun et al., 2016).”

“DL algorithms trained on large-scale pathological image datasets have demonstrated the ability to automate and streamline QC processes for HE slides [13, 14].”

“To mitigate data scarcity and improve generalisation, data augmentation has long been a standard component of medical image analysis pipelines.”

“In this study, we simulated two noise levels: the total counts of the images 10 6 and 10 5 .”

“Although clinical translation is still ongoing and respiratory effects were not predicted, Hooshangnejad et al.'s deepPERFECT demonstrated a quicker CT synthesis, thereby shortening the radiation planning workflow [7],[8].”

“Deep learning has reshaped medical imaging across CT, MRI, ultrasound, and histopathology for lesion detection, segmentation, and shape modeling [26].”

“Previous research has used several ML techniques including Support Vector Machines (SVM), decision trees, random forests, Artificial Neural Networks (ANNs) and ensemble classifiers, to classify breast lesions as malignant or benign [2] .”

“The models assessed include a seven-layer "AlexNet-CNN," [14] a shallower "CifarNet," [21], [22] and a variant of the "GoogleNet-CNN" [23].”

“These slices can be orthogonal patches that provide different views on the same anatomical point in multi-view fusion [62], three consecutive slices of the same imaging plane [46], or combinations of different imaging modalities [63].”

“Ciompi [20] the problem of insufficient clinical training data and easier access to the desired lung nodule features; Roth [21] et al. expanded the dataset by scaling, panning, and rotating in a randomized over-training sample and similarly added test samples for the detection of lymph nodes and…”

“5D image classification frameworks that achieve excellent detection results is the outcome of researching multiple path networks [14] [15].”

“Deep learning is now employed as efficient and accurate tool for Computer Vision, thereby the deep learning models rapidly deploy in every field of practical application for object detection, recognition as well as classification [5].”

“Our method achieves a gain of 4.77%, 0.0234, and 0.0303 over the second best method (Roth et al., 2015) in terms of mFROC score, maxF1 score, and AUC score respectively.”

“Formula (11) carries out the channel capacity test of the MIMO system with the number of antennas [1], [2], [4], [8], [14], where the number of antennas is [1] The SISO system can be regarded as a special case of MIMO system.”

“Its core idea is convolution calculation [9].”

“…imaging information in an end-to-end fashion under the usage of multi-stream convolutional neural networks (CNNs) to accommodate multiple sources of information (e.g., imaging and non-imaging data) or representations of the input (e.g., imaging modalities or multiple scales, orientations) [74, 75].”

“Implicit features could determine fibroids and polyps [1], and characterize irregularities in tissue morphology such as tumors [2].”

“2016 Roth et al.(25) Deep convolutional neural network Data from three institutions Sensitivity 75% at 3 FP Performance not good for small polyps”

“Exploring of pathology slides is an extremely complex assignment, requiring a long time to pick the skill up and mastering it [10] [14][21].”

“, lymph node [38] or end- diastole/systole frames in cine-MRI of the heart [19].”

“[34] submitted a cross-sectional method to segment MS lesions rely on deep 3D CNN with two interconnected pathways and shortcut connections.”

“As it is known, CNN is a representative network of deep learning and it has shown successful results in image feature extraction process [13][14].”

“Tümör bölümleme ve hastalık desen tanıma gibi zorlu görevlerde uygulanılan derin öğrenme modelleri ve özellikle de katlamalı sinir ağları insanüstü doğruluk ve etkinliğe ulaşmışlardır [8], [9].”

“To illustrate the effectiveness of 2-D CNNs, a similar framework was used in [71] for two other CT imaging applications: locating sclerotic metastases in spine imaging and lymph nodes and colonic polyps in cancer monitoring and screening.”

“…medical image deep learning publications (Anavi et al. (2015), Havaei et al. (2015),Bar et al. (2015), Roth et al. (2015a), Yan et al. (2015), Roth et al. (2015c), Shin et al. (2015), Wang et al. (2015), Xu et al. (2015), Srivastava et al. (2015), Srivastava et al. (2015), Zhang et al.…”