dataesr/scientific-paragraphs-categorization huggingface dataset:
B. ADAPTIVE ANCHOR BOX SIZES We propose an enhanced version of Faster R-CNN for lung nodule detection by employing a Mean-shift clustering technique  [32] , presented in algorithm 1. This method automatically learns scales and aspect ratios of anchor boxes from ground-truth nodule sizes in the training dataset. The learned anchor box configurations are used as hyper-parameter for the RPN to improve the performance of nodule candidate detection. We choose Mean-shift due to its robustness in feature space analysis  [12] , allowing to perform clustering without prior knowledge about the number of clusters. This is of importance since it is difficult for us to predict how many clusters of nodule sizes are included in the given dataset. This is also an advantage over manually hard-coded anchor boxes (e.
g. [23] ,  [24] ,  [33] ), or K-means-based anchor box clustering (e. g. [8] ) approaches. To the best of our knowledge, no work in the literature has investigated using Mean-shift clustering method to perform anchor box adaptation. Let's consider a dataset D having n nodule sizes presented by n bounding box sizes x i (w i , h i ), with i = 1,. , n, where w i , h i are the width and height of the bounding box x i , respectively. The algorithm divides D into a set of clusters {C j } such that the sizes of bounding boxes in the same cluster C j are similar. The center of each cluster is called mode and a bounding box size x i (w i , h i ) is a data point in a 2-D space. To do so, the algorithm finds high-density areas of data points x i (w i , h i ) in the training dataset D. A kernel K(x) is defined as a bounded function used to estimate the density of an area in D.
A. DATASET AND PREPROCESSING Training and testing data in our experiments are taken from the popular and publicly available dataset, LUng Nodule Analysis 2016 (LUNA16)  [14]. The LUNA16 dataset was created for the challenge "LUng Nodule Analysis 2016". It contains 888 thoracic CT scans using 17 different scanner models gathered from 1,018 cases of LIDC/IDRI database  [21]. These CT scans have slice thickness less than 3mm and a resolution of 512 × 512 pixels per slice. The data used in this work includes a total of 1,186 nodules of size >= 3 mm from LUNA16 dataset. These nodules are annotated by at least 3 out of 4 experienced thoracic radiologists. Each annotation contains the position (x, y, z coordinates in voxels) and the diameter of the nodule. For data preprocessing, input CT slices are clipped into an effective Hounsfield Unit (HU) range of [-1200, 600] and normalized into [0, 1] using a linear transformation.
State-ofthe-art lung segmentation techniques for pulmonary nodule analysis proposed in  [34]  are used to remove the background and extract two lungs from each CT image. This method consists of binarization (by thresholding), selection of the connected components corresponding to the lung, segmentation of the left and the right lung, computing the convex hull of each lung, dilating and combining two masks  [34]. In this work, we further dilate the segmented lung parenchyma by additional 10 pixels to avoid missed detection of juxtapleural nodules. Having 1,186 ground-truth nodules from LUNA16 dataset, we use 1,068 nodules (90%) for generating the training set, and the remaining 118 nodules (10%) are kept intact as the test set.
B. DATA ENHANCEMENT To avoid overfitting and improve model performance, we use two data enhancement methods: (1) adjacent slices and (2) data augmentation for generating new samples from the original 1,068 training ground-truth nodules in LUNA16 dataset. In the adjacent slice method, most pulmonary nodules are in spheroidal shapes while radiologists only annotated CT slices with the largest cross-sectional area  [3] , therefore we extract two upper and two lower adjacent slices of the ground-truth nodule image marked by the radiologists and add them to the training set. This method results in 5,210 lung nodule images and bounding boxes for the training set. In the data augmentation method, the above 5,210 nodule images are (1) horizontally flipped to have 10,420 nodule images for training the detection model at runtime inside the training loop and (2) randomly rotated from -10 o to +10 o , vertically and horizontally flipped, and translated ±10% in the horizontal and vertical direction in order to generate 40,473 nodule images prior to training the classification model.
C. MODEL TRAINING We train all of our models in the proposed architecture (Fig. 1 ) from scratch without pre-trained weights. Pre-trained models, although benefit from learned feature representations from the huge dataset that they were trained on, could not be very effective with nodules due to their special characteristics of shape, texture and sizes. First, we adapt anchor box sizes from the training dataset with the clustering adaptation method described in section III-B. We then train Faster R-CNN (including Feature Extraction Network, Region Proposal Network and RoI Classification Network) with the adapted anchor box. Finally, we train our False Positive Reduction network separately. In the nodule candidate detection stage, the input image dimension is 512 × 512 pixels, the same as a CT scan image.
Stochastic gradient descent (SGD)  [41]  is adopted to optimize the training objective. The network is trained for a total of 50 epochs with a batch size of 16. We used an initial In the false positive reduction stage, the input patch dimension is 64 × 64 pixels. A SGD optimizer is applied with an initial learning rate of 0. 1, weight decay of 0. 1, and a momentum of 0. 9. The network is trained for 50 epochs with batch size of 16 using binary cross-entropy loss function. We performed our experiments (both training and evaluation) on a local server, having an Intel Xeon 2620v3 (6 cores, 12 threads at 2. 4GHz), 32GB of DDR4 memory, and an NVIDIA GeForce GTX 2080 Ti (4352 CUDA cores and 11GB of memory). We implemented our CAD system using Pytorch 0. 4. 1 on Python 3. 7, along with CUDA 10. 2 for GPU acceleration on Debian Buster 10.
D. MODEL TESTING We employ the k-cross validation method for evaluating our model. In this method, we equally divide the dataset into k separated parts. Our evaluation is performed in k steps, each uses the k th part as test sets and the remaining k -1 parts are training set. In this paper, we use 10-fold cross validation to match with other works' evaluation protocol, making it easier and fairer to compare results. It is important to note that we include only original images without augmented data in the k th part as test set, to obtain accurate evaluation results.
1) Nodule Candidate Detection Results Table  1  shows results of Faster R-CNN based methods for nodule candidate detection task on LUNA16 dataset. From the table, it can be observed that our Faster R-CNN with adaptive anchor box achieved a sensitivity of 97. 5% at 2. 2 FPs/scan, which outperforms similar works with the highest sensitivity at very low false positive rate. For instance, the second-highest sensitivity is 96. 8% (a 0. 7% difference with ours) by  Wang et al. , 2019 [24] , but at 27 times more false positives. For another comparison, ISODATA (a K-meansbased clustering method) for anchor box sizes, proposed in  [8] , achieved 91. 4% (a 6. 1% difference with ours) at 3. 19 FPs/s (1. 45 times more false positives than ours). These results imply that the adaptive anchors generated by our Meanshift based method are more effective for nodule candidate detection than other manually designed anchors and other clustering methods, such as K-means or ISODATA.
3) Performance Comparison with Other Methods Table  4  shows a comparison between our proposed system with other recent nodule detectors on LUNA16 dataset. From this table, it can be observed that our method gains lower sensitivity than  [24] ,  [19] , and  [44]  at very low false positive rates of 1/8, 1/4, 1/2, respectively. From the false positive rate of 2 to 8, our method achieves the best sensitivity performance among the other works. Overall, our work obtains the highest sensitivity of 97. 1% at only 2 false positives per scan. It can also be observed from Table  4  that our proposed system achieves the CPM score of 88. 2%, being at the middle among the other works. Our methods are only 2. 4% less accurate than the top performer at 90. 6%  [45] , while still have a considerable gap with the other end (  [23]  and  [46]  at 79.
4) Generalization with other dataset To verify the generalization of our model for lung nodule detection on unseen data, we perform an additional evaluation on another independent dataset, called SPIE-AAPM  [15]. It should be noted that our model was trained only on the LUNA16 dataset, while tested on the SPIE-AAPM dataset for verification of generalization. SPIE-AAPM dataset aims at providing common CT scan data for research groups to develop quantitative algorithms for lung nodule detection and classification. It consists of 83 nodules from 73 CT scans. The provided ground-truth labels include nodule locations and diagnoses as benign or malignant. After performing data preprocessing, we used 72 nodules from the SPIE-AAPM dataset for additional evaluation. It should be noted that equipment manufacturers for collecting CT scans of the two datasets (LUNA16 and SPIE-AAPM) are very different.
This leads to a high variability in the data characteristics of the two datasets, and therefore, could considerably affect the model's performance. Table  5  presents FROC performance and CPM score of our proposed system and compares the results with other methods on SPIE-AAPM dataset. From the table, it can be observed that even with unseen data from SPIE-AAPM dataset, our method achieves a sensitivity of 89. 3% and a CPM score of 84. 8% which outperforms other nodule detection methods  [45] ,  [50]  on the same dataset. These results demonstrate the generalization of our proposed model.
V. DISCUSSION In this study, a novel lung nodule detection system based on Faster R-CNN with adaptive anchor box is proposed. Compared to other recent state-of-the-art detection methods, our proposed system achieves better sensitivity with a lower false positive rate. The results show that learning anchor box parameters from ground-truth nodule sizes has a good impact on the performance of the nodule detection model. In the nodule candidate detection stage, our system achieves a sensitivity of 97. 5% at 2. 2 FPs/scan on LUNA16 dataset, which are higher other recent Faster R-CNN based methods. The results suggest that our Mean-shift based adaptive anchor box Faster R-CNN yields better performance than other manually configured anchor box methods and other clustering-based methods such as K-means or ISODATA.
After the false positive reduction stage, our proposed residual convolutional neural network helps in reducing false positive rate from 2. 2 to 1. 72 while also slightly decrease sensitivity from 97. 5% to 95. 6%. Overall, the results imply that our method is a high-quality detection model with 95. 6% sensitivity at a low false positive rate of 1. 72, better than other recent state-of-the-art lung nodule detection models, and with a decent CPM score of 88. 2% on LUNA16 dataset. An additional evaluation on SPIE-AAPM dataset confirms that our system generalizes well on unseen data even with a vast difference of nodule characteristics compared to trained nodules from LUNA16 dataset. Besides, experiments on different nodule sizes demonstrate that our model is effective for lung nodule detection, especially for small ones, which is important for the early screening and diagnosis of lung cancer.
VI. CONCLUSION We have presented a novel CAD system for lung nodule detection in CT scans based on Faster R-CNN with adaptive anchor box. We have shown that anchor boxes automatically learned from ground-truth nodule sizes in the training dataset by Mean-shift clustering algorithm are effective for the nodule candidate detection task. In the false positive reduction task, the proposed ResNet model helps in reducing false positive rate in the trade-off of decreasing some sensitivity. With the results of 95. 6% sensitivity at 1. 72 FPs/scan and a CPM score of 88. 2% on the publicly available LUNA16 dataset. Our False Positive Reduction network obtained 93. 8% sensitivity, 97. 5% specificity, 95. 7% accuracy, 95. 5% F1-score and 0. 957 AUC. These results show that our proposed method is effective for lung nodule detection, hence helps to enhance the performance of early diagnosis of lung cancer.
Although our method shows good improvement in nodule detection performance, there are still several drawbacks. Firstly, the dataset used for training and testing our model is still limited with only 1,186 ground-truth nodules from LUNA16 dataset and 72 nodules from SPIE-AAPM dataset. Therefore, investigating more training and testing data could be beneficial for further boosting performance of this work. Besides, our current nodule detector is based on 2D CNN models. Thus, exploiting 3D CNN networks could be another interesting direction to continue our work. This direction, however, may increase the network complexity in the tradeoff of improving sensitivity performance.
FROC performance and CPM score of proposed system compared with other methods on SPIE-AAPM dataset. The detection sensitivity is measured at seven false positive rates: 1/8, 1/4, 1/2, 1, 2, 4 and 8. The best performance achieved at each false positive rate is highlighted in bold. Gupta et al. , 2018 [50] 19. 4 30. 5 44. 2 62. 8 64. 0 64. 0 66. 3 50. 1 Gong et al. , 2020 [45] 12. 0 37. 3 51. 8 69. 9 77. 1 85. 5 92. 8 60. 9 Ours 75. 5 80. 0 83. 7 88. 0 88. 8 88. 8 89. 3 84. 8.
Another dense sub-graph identification algorithm is the Restricted Neighborhood Search Clustering (RNSC)  [12]. It is a clustering algorithm that uses local search methods to attractively improve a clustering on a graph. It explores the solution space to minimize a cost function, calculated according to the numbers of intra-cluster and inter-cluster edges. Beginning from an initial random solution, RNSC iteratively moves a vertex from one cluster to another if this move reduces the general cost. When a number of moves has been reached without decreasing the cost function, the program ends  [12]. Given a PPI network represented as a graph that contains vertices representing proteins and edges representing protein interactions, these algorithms can discover sub-graphs or clusters based on different topological properties such as density, k-cores, core-attachment structures and peripheries.
GA-PPI-Net Approach GA-PPI-Net is an evolutionary algorithm, that aim at identifying communities of proteins  [3]. It allows to find communities having different sizes using the interaction and similarity criterion. It uses the similarity measures as well as the interaction measure between proteins and tries to find the best protein community by maximizing the concept of community measure. It is based on the concept of community score which it allows to combine the semantic and the interaction criterion. The community measure denoted F of a solution S is computed using equations 1. F(S ) = W 1 AVGS im(S ) + W 2 AVGInteraction(S ) (1) where: • W 1 and W 2 : weights ∈ [0, 1]. • AVGS im(S ) = i, j ∈ [1,n], i j S I M GS 2 (G i , G j )/n (2) Where: i) G i and G j are two different genes in the community S ; ii) n: the size of the community S ; iii) S I M GS 2 (G i , G j ): the similarity value between two genes (G i , G j ) in S , it is calculated using the semantic similarity measure GS2  [28] ; • AVGInteraction(S ) = i, j ∈ [1,n], i j InteractionValue(G i , G j )/n (3) Where: InteractionValue(G i , G j ) is the value of an interaction between two genes (G i , G j ) in S extracted from STRING Database  [16].
Contending algorithms We compared the performance of the GA-PPI-Net  [3]  approach with three clustering contenders, including: Markov Clustering (MCL), RNSC and ClusterOne. We prioritized the inclusion of these clustering approaches which have implementations in the public domain and do not rely on availability of additional knowledge to facilitate fair comparison. We used the CDAP package. It is an online package that contains the implementation for these protein communities detection methods. It can be accessed from http://www. eslahchilab. ir/sofwares/cdap. It ranks algorithms on various PPI datasets such as Collins, Gavin, Krogan-core and Krogan-extended. And, it is based on three well-known gold standard: MIPS, SGD and CYC2008. The functionality of the package can be easily extended to incorporate new datasets, new gold standards and compare novel methods with previously developed methods.
There are many types of biological pathways such as metabolic pathways or Gene-regulation pathways. To test the GA-PPI-Net approach and compare its performance with contending algorithms, we used PPI networks and gold standards of an existing protein community corresponding to human space. We used a real communities existing in KEGG Pathway Database  [11]. The KEGG Pathway Database (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions  [11]. To get the interaction between genes, we use the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database.
It is a biological database and web resource of known and predicted PPI. It contains information from several sources, including experimental data, computational prediction methods and public text collections  [16, 31]. This database presents the couples of proteins that are interacting, the mode of interaction between these couples and the interaction score which defines the number of citations of this interaction in the literature. To get the genes' annotation, the Gene ontology (GO) is used. GO describes the knowledge of the biological and biomedical domain with respect to three aspects  [35] : i) Cellular Component (CC): the parts of a cell or its extracellular environment; ii) Molecular Function (MF): the elemental activities of a gene product at the molecular level, such as binding or catalysis; iii) Biological Process (BP): operations or sets of molecular events with a defined beginning and end, pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms.
Validation Protocol The GA-PPI-Net  [3]  was evaluated with the three clustering methods (MCL, RNSC and ClusterOne) on five widely used sets of real PPI network data. These datasets can be collected from the KEGG pathway database and representing the network of protein interaction in the human spaces. In order to evaluate GA-PPI-Net to successfully detect communities in a PPI network, we use randomly selected proteins that are present in known communities from the reference pathway database KEGG. More precisely, this approach has been tested with five datasets. In total, we have 595 genes by removing the redundant genes. These datasets correspond to real and existing communities and are collected from the KEGG pathway database. We run GA-PPI-NET 20 times with proteins chosen randomly from the five datasets.
And, we retained each time the best community detected by GA-PPI-Net. Thus, we have 20 best communities with sizes varying from 5 to 40. The clustering methods (MCL, RNSC and ClusterOne) was run on CDAP by incorporating the five proposed datasets and KEGG pathway that contain a real and existing communities as a gold standard. Then, we filter the identified communities in order to obtain communities having size more than five. We obtained 23, 21 and 28 clusters of MCL, RNSC and ClusterOne respectively. Then we choose randomly 20 clusters having different sizes of each methods in order to evaluate and compare them with GA-PPI-NET.
Performance measures The predicted community based on the clustering obtained by the selected computational approaches (MCL, RNSC and ClusterOne) were compared with those obtained by the GA-PPI-Net approach. First, we check if the identified communities exist in real biological pathway databases such as KEGG, BIOCARTA, Reactome, EC-Number and BBid. This checking is achieved by the DAVID tools, which compare the predicted communities with others in different databases and gives the percentage of proteins that belong to the existing communities in those databases. DAVID bioinformatics resources consist of an integrated biological knowledge-base and analytic tools that aim at systematically extracting biological meaning from large gene/protein lists. It is the most popular functional annotation program used by biologists  [29].
It takes as input a list of proteins and exploits the functional annotations available on these genes in a public database such as, KEGG Pathways Biocarta, Reactome, BBID and EC Number, in order to find common functions that are sufficiently specific to these genes. Moreover, we use the semantic similarity to evaluate these approaches. Functional similarity between two proteins can be assessed by semantic similarity of their respective Gene Ontology annotation terms  [3]. We employed the GS2 measure (GO-based similarity of gene sets) to determine similarity between protein pairs in a given community. GS2 quantifies the similarity of the Gene Ontology annotations among a set of proteins by averaging the contribution of each all gene's Gene Ontology terms and their ancestor terms with respect to the Gene Ontology vocabulary graph  [28].
Results and discussion In this section, we comprehensively evaluate and compare the performance of the GA-PPI-Net  [3]  with the three clustering methods (MCL, RNSC and ClusterOne) on five widely used sets of real PPI network data. They include: (i) Apoptosis (88 genes), (ii) B cell receptor signalling (75 genes, (iii) Purine metabolism (159 genes), (iv) Rna degradation (159 genes) and (v) Oocyte meiosis (114 genes). In total, we have 595 genes by removing the redundant genes. These Data sets can be collected from the KEGG pathway database and represent the network of protein interaction in the human space. We use these datasets to evaluate the capability of these algorithms to extract relevant communities from high-throughput datasets. As introduced in section 4, the clustering methods chosen for the experimental study are MCL, RNSC and Clus-terOne methods.
The MCL and RNSC parameters are defined as proposed in the study of Brohée  [4] , where the Inflation parameter for MCL is set to 1. 8. MCL parameters are summarized in Table  1. For ClusterOne, we use the default parameters values. Concerning the GA-PPI-Net parameters, they are defined with an optimized set of values presented in  [3]  and given in Table  1. In this study, we run these clustering methods on CDAP by incorporating the five proposed datasets and KEGG pathway that contain real and existing communities as a gold standard. Then, we filter the identified communities in order to obtain communities having size more than five. For the GA-PPI-Net approach, we run AG 20 times with proteins chosen randomly from the five proposed datasets. And, we retained each time the best community.
A thorough comparison is not easy because the identified communities for propositions haven't the same sizes and the same constitution. Hence, the same datasets were used and we get 20 communities with diverse sizes varying from 5 to 45. To inspect the performance of three clustering methods and the GA-PPI-Net with respect to structural, functional quality and biological relevance of the resulting communities, we first, evaluate this communities by checking if they exist in KEGG or in other biological pathway databases. Each new identified community is presented to the DAVID tools, which compares this community with others in different databases and gives the percentage of proteins that belong to the existing communities in those databases. Table  2  estimates the recovery rate of the found communities with existing communities in different biological databases.