dataesr/scientific-paragraphs-categorization huggingface dataset:
At last, the 451 support vector machine (SVM) classifies the features into a 452 set of classes. Girshick et al. [76] modified the architecture 453 of R-CNN [74] with RoI pool (Region of Interest Pooling) 454 and named the network Fast RCNN. The RoI Pool (Re- gion of Interest Pooling) has improved and increased the 570 PROMISE-12 and NCI-ISBI-2013. Zhu et al. [90] have in- 571 troduced a 2D+3D hybrid network for better segmentation 572 of the prostate MRI using 3D MRI data. Vikal et al. [91] 573 performed 3D MRI volumes segmentation using a statistical 465 574 appearance shape model (3DASM). The statistical shape 466 575 ROI to extract features for predicting an object mask in a model output is compared with the manual segmentation. 467 576 pixel-to-pixel manner, in parallel with the existing branch Two different locally collected datasets having 33 and 15 468 577 for bounding box recognition.
The deep networks is trained with dice loss 926 function which performed better better in term of zonal 927 segmentation using depth separable convolutions. 928 Jia et al. [158] developed a novel technique called hybrid 939 DeepLabV3+ network with backbone ResNet18 [62] is 940 implemented by Khan et al. [160] for zonal segmentation of 941 prostate MRI using NCI-ISBI2013 dataset. In [161] Ghavami 942 et al. evaluated six different deep convolutional neural net-943 works for segmentation of prostate T2W MRI images. Yan 944 et al. [162] has developed a network called propagation deep 945 neural network (P-DNN) to combine multi-level features as 946 a single model. The P-DNN network utilized the convolution 947 and pooling layers (CP-layer) to extract high-level features 948 to accurately identify the prostate position and shape identi-949 fication.
In [171], Zhu et al. developed a novel network MRI images in multistep manner. Khan et al. [173] evaluated encoder-decoder network for segmentation prostate and its zone. The images of the dataset are centre cropped, normalized, and 2D patches are extracted to achieve better 999 segmentation. 1000 Liu et al. [174] have presented a novel shape-aware meta-1001 learning scheme (SAML) for segmentation of the medical 1002 images. The SAML approach roots in the episodic training 1003 technique of meta-learning, to facilitate robust optimization 1016 Lu et al. [177] developed a cascaded dual attention net-1017 work (CDA-Net) for segmentation of prostate MRI scan. The 1018.
OVERVIEW OF CLINICAL APPLICATION OF DEEP LEARNING FOR PROSTATE MRI SEGMENTATION AND CANCER DETECTION Table 7, the GAN-based prostate segmentation Dataset Performance Paper No. Year Pv Pro 12 NCI ISBI No of Images Pre-processing Technique Technique WG PZ CG AC DSC or Accuracy [187] 2017 152 •Upsampling •Registration •FCN •GAN 0. 41 ± 0. 28 [189] 2019 120 •Data augmentation •GAN 0. 864 [190] 2019 50 •Denoising •cGAN 0. 734±0. 18, 0. 778±0. 15, 0. 742±0. 16 •U-Net [191] 2020 50 •Resizing •NSGAN •Difficulty-aware 0. 909±0. 02, 0. 901±0. 01 1191 •Attention mechanism [192] 2019 60 •Normalization •Resizing For better contrast of prostate tissue, DCE MRI is used •DGMNet 1192 0. 93±0. 12 [188] 2019 50, 60 •Centre cropping •Bias field correction •standardization 1193 1194 •3D-APA-Net since it exploits the vascularity characteristic of tissues [199].
Prior to scanning the DCE MRI, intravenous contrast media, 0. 894±0. 1, 0. 864±0. 02, 0. 860±0. 01 [196] 2020 99, 50 •Resizing •Data augmentation normally gadolinium-based, is inserted into the patient. In •SegDGAN 1195 0. 925, 0. 889 •Scaling comparison to T2-W MRI alone, DCE MRI with T2-W MRI •U-Net 1196 0. 76±0. 10, 0. 73±0. 20, 0. 789±0. 12, [195] 2020 60 •Zero padding •Data augmentation •CycleGAN has shown to improve sensitivity of prostate cancer detection. 1197 •cGAN 0. 72±0. 03, 0. 70±0. 03, 0. 757±0. 02 [16] 2020 50 1198 Furthermore, the use of magnetic resonance spectroscopy •cGAN 0. 889 (MRSI) in conjunction with MRI has shown to improve Dataset: Pv-Private, Pro-12-PROMISE12, NCI-13-NCI-ISBI-2013. 1199 identification and diagnosis of prostate cancer [200]. nance images of PROSTATEx Challenge and PROSTATEx-1261 ically insignificant prostate cancer, and improve detection of 2 Challenge to perform computerised classification of clin-1262 clinically significant cancer, which is defined as the one with ically relevant prostate lesions and computerised evaluation 1263 biopsy Gleason score greater than 7 [204], [205].
In short, the 1216 TDN is for automated prostate detection and registration of 1217 T2W and ADC whereas the dual-path CNN is for clinically 1218 significant (CS) prostate cancer detection. Huang et al. [139] 1219 developed U-Net based network for segmentation of prostate 1220 cancer region in mp-MRI. The mp-MRI of T2W and ADC aided diagnosis (CAD) algorithm using mp-MRI is shown to increase sensitivity but it decreases specificity, giving more false positives [203]. The lack of a public mp-MRI dataset of the prostate with well-defined performance indicators to compare mp-MRI CAD systems has been a major roadblock to development. VIII.
There are mainly 3 groups of 1440 dataset depending on ground truth label; solely on segmenta-1441 tion, solely on CaP classification or staging and combined 1442 segmentation and classification. The PROMISE12 [206], 1443 NCI-ISBI-2013 and QIN-PROSTATE Repeatability are only 1444 for segmentation but the I2CVB [33] can be used for devel-1445 opment of zonal segmentation and cancer detection methods. 1446 On the other hand, both PROSTATEx and PROSTATEx-1447 2 are used for development of diagnostic classification of 1448 clinically significant prostate lesions, whereas the ground 1449 truth is labelled using 5-class of Gleason Grade Group which 1450 was graded by an experienced pathologist [202]. Basically, 1451 the PROSTATEx is a subset of PROSTATEx-2. Both chal-1452 lenges aim to promote the advancement of image based 1453 computational techniques and thereby reducing unnecessary 1454 biopsies.
The dataset summarized in Table 9 can be found in 1455 their corresponding websites as listed below. 1456 1) https://PROMISE-12. grand-challenge. org/. 1457 2) https://wiki. cancerimagingarchive. net/display/DOI/NCI-ISBI 1460 3) https://i2cvb. github. io/. 1461 4) https://wiki. cancerimagingarchive. net/display/Public/SPIE-1462 AAPM-NCI+PROSTATEx+Challenges. 1469 collected from Universiti Kebangsaan Malaysia Medical 1470 Center (UKMMC) is available at http://bit. ly/prostatehukm. 1471 The images were acquired using a 3-Tesla Siemens TIM 1472 MRI scanner with surface coil. The image dimensions are 1473 384×384 and 320×320, with thickness of 3 mm. The dataset 1474 image masks are labeled for 2 classes, whole prostate gland 1475 20 VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.
The main problem in research on prostate cancer diagnosis is the availability of a large dataset with precise annotation 1521 by the radiologist and pathologist. Especially, the process of 1522 annotation of prostate MRI needs sufficient effort to find the 1523 pathology in the MRI images. Based on this survey, it is apparent that the ability of DCNN 1527 to learn the appropriate features of input images has given 1528 them an edge over traditional methods of prostate MRI 1722 cancer based on magnetic resonance image analysis," in 2008 3rd Inter- national Symposium on Communications, Control and Signal Processing. 1524 1525 X. FUTURE CHALLENGES, PERSPECTIVE AND 1526 CONCLUSIONS with deep convolutional neural networks," in Advances in neural infor- 1549 1529 segmentation. The benefit is pre-processing steps: data aug-1530 mentation, generative adversarial network, and patch extrac-1531 tion provide sufficient data to enhance the performance of 1532 DCNN networks.
Moreover the availability of public prostate 1533 datasets and the development of advanced deep neural net-1534 works as well as powerful hardware for computing play vital 1535 roles in better prostate segmentation. 1536 This review article has presented an overview of the seg-1537 mentation work of prostate MRI covering the traditional 1538 method of atlas and deformable model as well and DCNN 1539 methods. The background provides information regarding 1540 the anatomy, carcinoma and prostate screening and MRI 1541 imaging techniques. The methods used for preprocessing of 1542 MRI dataset and prostate segmentation are explained in detail 1543 in the literature. Moreover, current challenges and possible 1544 future research directions have given insight challenges and 1545 future perspective.
For example, we show that, under 1 ACCEPTED MANUSCRIPT -CLEAN COPY certain conditions, doubling the flow rate reduces the capture rate by four orders of magnitude. Additionally, we provide the particle distribution in the liquid along the channel, resulting from this competition, for different N LD values. We believe that this work opens new perspectives for analysis and biotechnology applications. More precisely, the proposed model should extend to any transverse force that can be written in the form of a potential energy.
However, these lift effects, even weak, ACCEPTED MANUSCRIPT -CLEAN COPY may play a role at low Reynolds numbers in straight channels, a geometry commonly used in microfluidic applications. In this paper, we will show that it is the competition between lift forces and diffusion that sets the particle capture efficiency. By presenting a comprehensive approach, including experiments, numerical simulations and a model, we show that the critical effect of lift forces leads to a reduced capture rate by focusing particles far from the functionalized surface. We introduce a dimensionless parameter that compares the relative importance of diffusion and lift forces, called the Lifto-Diffusive Number N LD , that is the relevant parameter allowing to fully describe the problem. Using an energetic approach, the particle concentration profile is derived.
The coating ensures that the electrostatic interactions take place only on the capture surface, which has been observed experimentally as no capture events were observed ACCEPTED MANUSCRIPT -CLEAN COPY outside of the gold surface. NaCl (SigmaAldrich) is added to the solution at a concentration of 10 -2 M, reducing the electrostatic interactions range to 3 nm  [ 32]. The capture surface is of width W s ∈ [50 ; 270] µm and length L ∈ [50 ; 250] µm and is located in the middle of the floor of the channel. As H W c and W s < W c -2H, a 1D Poiseuille flow profile over the capture surface is further assumed and corner effects can be disregarded. In a first step, the microfluidic device is filled with DI water. Then, at t = 0, the solution containing the particles is injected at a constant flow rate for the whole duration of the capture experiment.
ACCEPTED MANUSCRIPT -CLEAN COPY Up to our knowledge, the only universal theory concerns the case of molecule capture which is usually extended to nano and microparticules. Indeed, electrochemists have extensively studied the problem  [ 16, [38] [39] [40] [41] [42] [43]  which has been rationalized by Squires et al. [ 15]. In the framework of this theory, the collection regime for Pe H , Pe S 1 falls into what the authors call the partial collection regime. In this case, the capture rate is given by J 0 = 1. 468 Dc 0 W s Pe S 1/3 [ 16] , displayed as a solid line on Figure  3. While part of the experiments seems to be properly described by the convection-diffusion theory, most of the capture rates are way smaller than J 0. In light of this experimental result, additional effects have to be considered as we deal with particles instead of molecules.
Their numerical solution can be fitted by: v z Lift (z) = v * (2. 18 z -21. 23 z3 ) = v * f (z) where z = z/H, v * = 9 4 a 3 Q 2 νW 2 c H 4 is the characteristic lift velocity, at least for Re < 15 above which it shows a slight dependence in Re which we will not discuss. The function f (z) = 2. 18 z -21. 23 z3 is plotted on Figure  4a  and exhibits two stable equilibrium points at z ±0. 32. Consequently, in a system where there is no diffusion, all the particles are laterally displaced by the lift force up to one of these two ACCEPTED MANUSCRIPT -CLEAN COPY focusing positions. The lift force is computed considering that it is balanced by the viscous drag force 6πηav: F Lift = 6πηa v z Lift (z) = F * f (z) (1) where F * = 6πηav * = 27π 2 a 4 ρQ 2 H 4 W 2 c is the characteristic lift force amplitude.
ACCEPTED MANUSCRIPT -CLEAN COPY results are supported by Lagrangian simulations that agree quantitatively with the analytical model, illustrating the relevance of using the Maxwell-Boltzman distribution. Technical details of these simulations can be found in the supporting information, SI (see section S1). The numerical tool, LiDi, that we have developed to obtain these profiles is available at http://lidi. ens-rennes. fr/download. This tool has other functionalities, detailed in SI (see section S2).
Short Range Attractive Interactions The model considers that the particles do not undergo attractive or repulsive interactions. However, it may happen that short range interactions, such as attractive electrostatic ones, come into play and modify the above analysis  [ 32, 44, 45]. ACCEPTED MANUSCRIPT -CLEAN COPY In our experiments, particles are charged negatively and the capture surface is charged positively, thus electrostatic attractive interactions are present. Their amplitude varies with e (-d/λ D ) where d is the distance between two charged surfaces and λ D is called the Debye length  [ 32]. This length varies in water from 1 µm to 1 nm depending on the salt con- centration (λ D ∝ c -1/2 salt ), and represents the typical range of electrostatic forces. If, at the beginning of the capture surface, the lift-induced thickness h is smaller than λ D , as depicted in domain a of Figure  8 , the attractive force acts on out-of-equilibrium particles.
First, it is legitimate to ask whether this analysis is also applicable to deformable objects or macromolecules for example. For such objects, the difficulty lies in the definition of the lift force which depends strongly on the shape and the deformation of the object, and for which there is not always a consensus as for ACCEPTED MANUSCRIPT -CLEAN COPY example for deformable objects  [46] [47] [48]. These studies are therefore outside the scope of this study and offer significant prospects for further studies. More generally, this study should extent to any transverse force that can be written in the form of a potential energy (i. e. a force that derives from a position), the theoretical analysis, consisting of equations (  1 ) to (4) can be followed in a similar way considering the transverse force.
They show that the approach produces outcomes very similar to real morphological operations. Mellouli et al. [26]  performed a more extensive validation of the previous method, proposing different deeper networks that are used to perform image classification. In their experiments, the proposed network achieved promising results for two datasets of digit recognition. In  [23] , the authors proposed a new network capable of performing some morphological operations (including erosion, dilation, opening, and closing) while optimizing non-flat structuring elements. Their proposed network, evaluated for image deraining and de-hazing, produced results similar to those of a ConvNet but using much fewer parameters. Finally, Franchi et al. [24]  proposed a new deep learning framework capable of performing non-approximated mathematical morphology operations (including erosion, dilation) while optimizing nonflat structuring elements.
III. DEEP MORPHOLOGICAL NETWORKS In this section, we present the proposed approach, called Deep Morphological Networks (or simply DeepMorphNets), that replaces the linear convolutional filters with optimizable non-linear morphological operations. Such replacement allows the proposed network to capture distinct information when compared to previous existing models, an advantage that may assist different applications. However, this process raises several challenging issues. One first challenge is due to the linear nature of the core operations of the existing networks. The convolutional layers extract features from the input data using an optimizable filter by performing only linear operations not supporting nonlinear ones. Formally, let us assume a 3D input y(•) of a convolutional layer as a mapping from coordinates (Z 3 ) to the pixel-value domain (Z or R).
This architecture was designed to be used with the proposed synthetic Require: a minibatch of inputs and targets (y 0 , y * ), previous weights W , and previous learning rate α. Ensure: updated weights W. 1: 1. Forward propagation: 2: for k=1 to L do 3: {First Processing Unit Operation} 4: W b (1) k ← b(W (1) k ) 5: s k ← y k-1 * W b (1) k 6: (1) y k ← P (s {Second Processing Unit Operation} 8: W b (2) k ← b(W (2) k ) 9: s (2) k ← y (1) k * W b (2) k 10: y (2) k ← P (s (2) k ) 11: y k ← y (2) k * W ( g y k-1 ← g y (1) k W (1×1) k-1 , g W (1×1) k-1 ← g y (1) k y k-1 17: g y (2) k-1 ← g y k-1 W b (2) k-1 , g W b (2) k-1 ← g y k-1 y (2) k-1 18: g y (1) k-1 ← g y (2) k-1 W b (1) k-1 , g W b (1) k-1 ← g y (2) k-1 y (1) W (1×1) k ← W (1×1) k -αg W (1×1) k 23: W (1) k ← W (1) k -αg W b (1) k 24: W (2) k ← W (2) k -αg W b (2) k 25: end for datasets (presented in Section IV-A1).
A. Datasets Six datasets were employed to validate the proposed Deep-MorphNets. Two image classification synthetic ones were exclusively designed to check the feature learning of the proposed technique. Other two image classification datasets were selected to further verify the potential of DeepMorphNets. Finally, to assess the performance of the proposed technique in distinct scenarios, two pixel classification datasets were exploited. 1) Image Classification Datasets: Synthetic Datasets. As introduced, two simple synthetic (image classification) datasets were designed in this work to validate the feature learning process of the proposed DeepMorphNets. In order to allow such validation, these datasets were created so that it is possible to define, a priori, the optimal SE (i. e. , the SE that would produce the best results) for a classification scenario.
Hence, in this case, the validation would be performed by comparing the learned SE with the optimal one, i. e. , if both SEs are similar, then the proposed technique is able to perform well the feature learning step. Specifically, both datasets are composed of 1,000 grayscale images with a resolution of 224×224 pixels (a common image size employed in famous architecture such as AlexNet  [2] ) equally divided into two classes. The first dataset has two classes. The first one is composed of images with small (5×5 pixels) squares whereas the second consists of images with large (9 × 9 pixels) squares. Each image of this dataset has only one square (of one of the above classes) positioned randomly. In this case, an opening with a SE larger than 5 × 5 but smaller than 9 × 9 should erode the small squares while keeping the others, allowing the model to perfectly classify the dataset.
More difficult, the second synthetic dataset has two classes of rectangles. The first class has shapes of 7×3 pixels while the other one is composed of rectangles of 3×7. As in the previous dataset, each image of this dataset has only one rectangle (of one of the above classes) positioned randomly. This case is a little more complicated because the network should learn a SE based on the orientation of one the rectangles. Particularly, it is possible to perfectly classify this dataset using a single opening operation with one of the following types of SEs: (i) a rectangle of at least 7 pixels of width and height larger than 3 but smaller than 7 pixels, which would erode the first class of rectangles and preserve the second one, or (ii) a rectangle with a width larger than 3 but smaller than 7 pixels and height larger than 7 pixels, which would erode the second class of rectangle while keeping the first one.
UCMerced Land-use Dataset. This publicly available dataset  [33]  is composed of 2,100 aerial images, each one with 256 × 256 pixels and 0. 3-meter resolution per pixel. These images, obtained from different US locations, were classified into 21 classes: agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts. As can be noticed, this dataset has highly overlapping classes such as the dense, medium, and sparse residential classes which mainly differs in the density of structures. Samples of these and other classes are shown in Figure  5. WHU-RS19 Dataset. This public dataset  [34]  contains 1,005 high-resolution images with 600 × 600 pixels divided into 19 classes (approximately 50 images per class), including: airport, beach, bridge, river, forest, meadow, pond, parking, port, viaduct, residential area, industrial area, commercial area, desert, farmland, football field, mountain, park and railway station.
Exported from Google Earth, that provides highresolution satellite images up to half a meter, this dataset has samples collected from different regions all around the world, which increases its diversity but creates challenges due to the changes in resolution, scale, and orientation of the images. Figure  6  presents examples of some classes. 2) Pixel Classification Datasets: Pavia Centre. This publicly available dataset  [35]  is composed of one image, acquired by the Reflective Optics System Imaging Spectrometer (ROSIS), covering the city of Pavia, southern Italy. This hyperspectral image has 715×1,096 pixels, spatial resolution of 1. 3m per pixel, and 102 spectral bands. Pixels of this image are categorized into 9 classes. The false-color and groundtruth images, as well as the number of pixels in each class, are presented in Figure  7.
Pavia University. This public dataset  [35]  was also acquired by the ROSIS sensor during a flight campaign over Pavia. This hyperspectral image has 610×340 pixels, spatial resolution of 1. 3m per pixel, and 103 spectral bands. Pixels of this image are also categorized into 9 classes. The false-color and groundtruth images, as well as the number of pixels in each class, are presented in Figure  7. For both datasets, in order to reduce the computational complexity, Principal Component Analysis (PCA)  [36]  was used as a pre-processing method to reduce the dimensionality. Specifically, following  [37] ,  [38] , for both datasets, we selected the first 3 principal components, which explain, approximately, 99% of the total variance of the data.
B. Baselines Several baselines were employed in this work. The first one, exploited in both tasks and all aforementioned datasets, is a standard convolutional version of the corresponding Deep-MorphNet. This baseline, referenced as ConvNet, recreates the exact morphological architecture using the traditional convolutional layer but preserving all remaining configurations (such as filter sizes, padding, stride, etc). Moreover, this baseline makes use of max-pooling layers between the convolutions, which makes it very similar to the traditional architectures of the literature  [2]. An extension of this first Fig. 4 : The proposed morphological network DeepMorphNet conceived inspired by the AlexNet  [2]. Since each layer is composed of distinct types of morphological neurons, the number of each type of neuron in each layer is presented as an integer times the symbol that represents that neuron (as presented in Section III-B).