dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, data captured in the initial layers are features corresponding to lower semantic information that are extracted from the input and shared to the output. In concatenation, low-level information is shared between the input and output, and this information is directly shared across the network. This method allows feature compaction and high standardization, which is not possible using addition. Mu-Net achieves its highest Dice scores when a classic data augmentation strategy is used during its training. The ablation study confirmed that combining layer reduction and kernel size maintains high Dice scores in segmentation, whereas using only layer reduction does not. We propose combining the reduction layers and kernel size and hypothesize that concatenation allows transfer learning on essential features.
The classic data augmentation chosen here is supported by simple transformations such as reflections, rotations of 90 degrees or filtered whitening of images. GAN solutions are based on generative and discriminative networks, which have to be trained and optimized. If this behavior is observed with another dataset of bright-field images, this architecture could become more attractive (for avoiding using data augmentation strategies based upon GAN architectures, which is time consuming). A high rate of false positives is present in the samples chosen to illustrate the architecture results under classic data augmentation. A false positive is defined as an instance of an organoid region that is present in the predicted mask and has zero intersection with the ground truth mask region. The surroundings of BOs include spreading cells  [2]  that could be interpreted by these networks as organoid regions.
The ground truth could impact this result due to the small dataset used for training and the more shallow architecture, which could cause surrounding details to be approximately learned. Observations of the segmentation results using all data augmentation strategies in the same training dataset could be interesting. Other segmentation strategies do not achieve strong results. The main drawback observed in this study is the need to manually preset some parameters, rendering the segmentation process semiautomated. These unexpected scores are also linked with the fact that ML algorithms are inappropriate for datasets with strong variation  [33]. However, in the early developmental stage of BOs, no neuroepithelial formation is exhibited, whereas after 14 days, the BO shape is transformed by this structure  [22].
The algorithms are also influenced by batch syndrome: BOs grow differently in the same environment, resulting in one or more neuroepithelial zones  [1]. Thus, instead of of furnishing a preset parameter for each image in a particular dataset, ML solutions do not consider these cultures. The Kmean machine learning algorithm is an exception, achieving the best segmentation scores. By separating the number of parts to segment (here, the background and the BO), this algorithm does not require another setting that influences the score. The Kmean clustering algorithm uniquely compares the cluster containing a centroid with a pixel  [29]. Particle swarm optimization allows us to obtain a more precise clustering efficiency and thus a better accuracy than using a single Kmean algorithm. Mu-Net outperforms all ML strategies except Kmean.
Our reduced model of segmentation for a tiny dataset of BO images is automated and does not require presetting parameters. The light architecture allows the model to perform image segmentation almost as quickly as using a Kmean algorithm, producing similar results. However, this architecture currently needs to be improved for segmenting tiny BO datasets. An improvement could be to use prior information during Mu-Net training (such as providing shape or topological information on the kind of image segmented)  [34] , but this requires a better understanding of the morphological characterization of these cultures. The proposed Mu-Net architecture could also be compared with other U-Net variations, such as those described in  [7]. We observe the fundamental role of concatenation in this architecture; however, other U-Net variations are characterized by dense connections  [35].
Combining such an update with the lighter architecture to observe its contributions to the bright-field image segmentation of these cultures could be interesting. Additionally, we choose a supervised version (with manually labeled datasets as ground truth in this paper. In future comparisons, the identification of a zero-shot learning solution to decrease the time/labor of neurobiologists in manual segmentation could be interesting  [36]. Concerning the metrics comparison, we could calculate other known scores (F1, accuracy, etc. ) in future studies. Augmentation strategies could also be formed with nonlinear transformations or other GAN architectures  [37] , and other data augmentation strategies could be tested with semantically optimized segmentation using an optimized U-Net  [38]. We mention that the noise injection optimization benefit during training must be thorough.
We verify the contributions of the ablation study, which includes only a few configurations. In the future, conducting an ablation study on filtering, kernel reduction, or even other skip connections would be interesting. The main future objective is to reproduce these procedures on another bright-field dataset of BO images or on another biomedical culture (miBrain, cortical, etc. ) if no BO dataset is available. Concerning the dataset we use  [22] , a quantitative comparison of the segmentation quality at various stages (9, 14 and 15 days of development) and states (physiological and pathological) could be interesting. In this paper, we quantify the segmentation quality only for the entire dataset, proving that Mu-Net is robust for all states and stages. Once all the procedures are validated on other datasets, these reduced algorithms could also be used in biosensing, as described in  [39].
Conclusions In this article, we report the first results on Mu-Net, a light deep architecture for brightfield BO image segmentation in small datasets. The proposed segmentation architecture achieves state-of-the-art performance (U-Net) with a faster computation time than other architectures. Mu-Net also outperforms other computer vision segmentation algorithms and is particularly robust to various data augmentation strategies, unlike UNet-Mini. To consolidate these initial results beyond validating our methodology on other datasets, we also need to examine the particular contributions of combining a lightened architecture with other SOTA methodologies. We believe that optimization, including that of shape/topological information given during the data augmentation step to better train our model, could improve the segmentation.
Medical image segmentation is one of the most challenging tasks in medical image analysis which aims to extract effective information and improve the level of clinical diagnosis. In last decades, automatic segmentation based on Deep Learning (DL) models such as U-Net and CNN architectures have been widely used to automatically extract organs or lesions contours in order to overcome manual segmentation limitations. In this paper, we performed a comparative study between CNN and U-Net performance for medical image segmentation applied to cardiac-MRI segmentation using U-Net from short-axis MRI images of ACDC database. The adopted architecture was trained and tested with and without data augmentation. The obtained results show a strong agreement between the labelled masks and the predicted ones with a mean DSC that reached 97,9% and a mean Hausdorrf Distance (HD) that reached 5.
318 mm. A quantitative comparison was made on two levels. The first one is an intracomparison made between the adopted model and methods based on the same architecture and having been trained and tested using the same database which proved that our method reached the highest performance and can be considered as a promising tool for medical image segmentation. The second one is an inter-comparison made between U-Net and CNN performance which proved that U-Net is more suitable for carrying out this task since it takes less time for training as it does not have a fully connected layer and offers a fairly significant similarity to ground truth compared to CNN.
The proposed design was applied for various Computed Tomography (CT) scan images of lungs, MR images of brain and X-ray images. Xie et al. [8]  have proposed a novel framework that efficiently bridges a CNN and a Transformer (CoTr) for accurate 3D medical image segmentation. The proposed method was evaluated on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset that covers 11 major human organs. A deep fully CNN was proposed in  [9]  for both left and right ventricle segmentation in short-axis MRI. For the first time, as mentioned, the neural network was trained end-to-end in a single learning stage from the whole image inputs and ground truths to make inference at every pixel. It is worth to note that CNN mainly focus on classification tasks, take an image as input and produce a unique class label  [10].
[16]  have proposed a new variant of U-Net architecture called TransUNet. This latter employed Transformers and U-Net as a strong alternative for medical image segmentation to overcome U-Net limitations in explicitly modeling long-range dependency due to the intrinsic locality of convolution operations. In other research  [17] , 2D U-Net was used for the segmentation of the cartilage and the meniscus. Tao et al  [18] , have proposed a fully automatic method for left ventricle (LV) quantification and segmentation from Cine -MRI images. Three CNNs with U-Net were trained on specific databases and tested on an independent multivendor and multicenter data set of 196 patients. This research frame is organized as follows. The proposed methodology is explained in Section. 2 including data description and augmentation and the adopted architecture for the heart segmentation.
Data description To train and test the U-Net neural network, we used the Automated Cardiac Diagnosis Challenge (ACDC) database (MICCAI challenge, 2017)  [19]. ACDC dataset contains Cine-MRI images of 150 patients acquired in the short-axis orientation. It is composed of two datasets: The training dataset which is composed of 100 patients and the testing dataset which is composed of 50 patients. For all patients of the training database, clinical information and corresponding labelled masks were provided. In our study, we converted the 3D-images already stored in NIFTI format to 2D-DICOM format using an open-source toolkit called X-MedCon. Fig. 2  shows an example of the ACDC database (healthy patients) before and after conversion.
Data augmentation Based on the literature, Deep Learning techniques have greatly improved classification and segmentation accuracy "thanks to their capability to handle complex conditions"  [20]. To achieve a good generalization performance, DL models rely heavily on big data. However, for many application areas, such as medical analysis, access to large database is limited as the data annotation is expensive and often requires significant amounts of expertise  [21]. In This context, the most popular technique largely used to increase the size of the training dataset is data augmentation. In our study, we adopted some data augmentation techniques such as geometric transformations (rotation, translation, reflection) and opening and closing morphological operation using an open-source Python library called TorchIO.
Experimental results and discussion The proposed method was trained and tested using python version  (3. 7). All experiments are conducted using a single Nvidia RTX2080Ti GPU. For training, we used the Dice loss function which was minimized using Adam optimizer  [23]  and RMSProp. At the beginning of the experiments, we selected the hyper-parameters used for the training of the U-Net architecture. Then, our neural network was trained to segment both of LV and RV.
Hyper-parameters selection In this sub-section, we present the experiments carried out to select hyper-parameters for training phase such as learning rate (lr), epochs and optimization algorithms. We started with a lr of 1*10 -2 then we repeat the experiments with a lr of 1*10 -3. In Table. 1, we present the algorithm's performance for a lr of 0. 01 and 0. 001 with mini-batch size of 4 over 10, 20, 50 and 80 epochs. As shown in Table. 1, our model achieved the highest performance with ADAM optimizer (92. 77%) and was trained for 80 epochs which were enough for convergence with a lr of 0. 001 and a mini-batch size of 4. Fig. 4  presents the mini-batch accuracy and mini-batch Dice loss during training phase with and without data augmentation. By increasing database as shown in Fig. 4 , the accuracy was improved from 96.
Comparative study with state-of-the art methods Compared to the standard U-Net architecture, the proposed method reached an accuracy of 98. 79% in terms of DSC against 78. 75%. In terms of training time, it was mentioned that the standard U-Net architecture took about 10 hours while our model took almost 2h 40 min. The segmentation results proved that the proposed method is characterized by better performance compared to the standard model. To better evaluate the model performance, we made a quantitative comparison with state-of-the-art methods (Table. 4). Comparison with the methods having used the same database show that the proposed method reached the highest performance with a mean DSC of 97. 9 % and a mean HD of 5. 318 mm for LV segmentation. However, the method proposed by Isensee et al  [25]  reached the smallest HD (8.
Conclusion In this paper, as part of the comparison between U-Net and CNN performance for medical image segmentation, U-Net model was adopted for left and right ventricle segmentation from short-axis MRI images with and without data augmentation. A Comparative study with the state-of-the-art methods based on U-Net and CNN architectures shows that the proposed method has achieved the highest performance in terms of dice similarity coefficient, Hausdorrf distance and accuracy. This is a significant step towards reducing manual segmentation limitations performed by clinical experts during the diagnosis of cardiac diseases. Comparative study with CNNs models shows also that U-Net is more appropriate for medical segmentation tasks.
Table. 4. Performance in the MICCAI2017 test set. Year Database LV RV Mean DSC (%) Mean HD (mm) Mean DSC (%) Mean HD (mm) ED ES ED ES ED ES ED ES Isensee et al 2018 ACDC 96. 7 92. 8 5. 476 6. 921 95. 1 90. 4 8. 205 11. 605 Zotti et al 2019 ACDC 96. 4 91. 2 6. 180 8. 386 93. 4 88. 5 11. 052 12. 650 Zhongyu et al 2019 ACDC 94. 2 6. 641 90. 3 13. 830 Calisto et al 2020 ACDC 95. 8 90. 3 5. 592 8. 644 93. 6 88. 4 10. 193 12. 234 Simantiris et al 2020 ACDC 96. 7 92. 8 6. 366 7. 573 93. 68 88. 9 13. 289 14. 367 Our method 2022 ACDC 97. 9 94. 7 5. 318 7. 812 95. 75 92. 81 8. 369 10. 234.
Understanding the cooling service provided by vegetation in cities is important to inform urban policy and planning. However, the performance of decision-support tools estimating heat mitigation for urban greening strategies is not systematically evaluated. Here, we develop a calibration algorithm and evaluate the performance of the Urban Cooling model developed within the open-source InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) software. The Urban Cooling model estimates air temperature reduction due to vegetation based on four predictors: shade provision, evapotranspiration, albedo, and building density and was designed for data-rich and data-scarce situations. We apply the calibration algorithm and evaluate the model in two case studies (Paris, France, and Minneapolis-St Paul, USA) by examining the spatial correlation between InVEST predictions and reference temperature data at 1-km horizontal resolution.
, 2018). Such models require long development and calibration processes and therefore significant human and time resources. The same applies to geostatistical models that require large geospatial datasets (e. g. ,  Cheung et al. , 2021). On the other hand, an increasing body of literature in the fields of urban ecology and ecosystem services science examines the cooling service through indicators associated with land use  (Derkzen et al. , 2015; Farrugia et al. , 2013; Larondelle & Haase, 2013; Nedkov et al. , 2017; Zardo et al. , 2017) , as reviewed in previous work  (Hamel et al. , 2021). These models further simplify the physical processes underlying the cooling dynamics, the effect of the morphological parameters of the buildings, and the daily or seasonal temperature variations, often by attributing a "cooling capacity" to a broadly defined land use type.
e. applicable in any geography, and flexible enough to represent different types of blue-green infrastructure and decision contexts  (Hamel et al. , 2021). InVEST (Integrated Valuation of ecosystem services and tradeoffs) is one of the most popular tools developed to support ecosystem services assessments by quantifying and mapping the benefits provided by blue-green infrastructure in urban or non-urban environments  (Natural Capital Project, 2022). The Urban Cooling model within InVEST, developed by the author team, addresses some of the limitations stated above by estimating air temperature (instead of land surface temperature only), being applicable in cities all around the world by using readily available data. The InVEST Urban Cooling model has recently been applied to a European urban dataset  (Cortinovis et al.
, 2022) , and to individual cities in Europe, and India  (Bosch et al. , 2021; Kadaverugu et al. , 2021; Zawadzka et al. , 2021). However, attempts to validate the model against observed or modelled data are much rarer. In previous work,  Bosch et al. (2021)  found good performance of the model in Lausanne, although the observation data was sparse with 11 weather stations over 112 km 2. Zawadzka et al. (2021) found that the model explained between 48% and 60% of the variability in land surface temperature for a Summer day in three towns in the United Kingdom. Despite these notable efforts, the model has not been extensively tested and validated against spatialized air temperature data. The goal of this paper is to evaluate the performance of the InVEST Urban Cooling model in different contexts: daytime and nighttime, and with different levels of data availability.
Model description The InVEST Urban Cooling model, hereafter "InVEST model", is fully described in the InVEST software User's manual  (Natural Capital Project, 2022). We provide a summary of key equations here to orient readers. The model computes air temperature prior to air mixing (𝑇 𝑛𝑜𝑚𝑖𝑥 , in degree Celsius) on each pixel as a function of a background rural reference https://doi. org/10. 5194/egusphere-2023-928 Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0 License. temperature (𝑇 𝑟𝑒𝑓 ) modified by a local heat factor. The latter is expressed as the maximum urban heat island intensity for the city (𝑈𝐻𝐼 𝑚𝑎𝑥 ) modulated by the local heat mitigation (𝐻𝑀), such that, for a given pixel i: 𝑇 𝑛𝑜𝑚𝑖𝑥 (𝑖) = 𝑇 𝑟𝑒𝑓 + (1 -𝐻𝑀(𝑖)) × 𝑈𝐻𝐼 𝑚𝑎𝑥  [1]  While this temperature does not account for mixing due to atmospheric turbulence, actual air temperature, 𝑇 𝑎𝑖𝑟 , is estimated from 𝑇 𝑛𝑜𝑚𝑖𝑥 using a spatial moving average algorithm with search radius rmix.
Paris metropolitan area Input data. LULC data for the Île-de-France region were obtained from the regional urban planning agency (Institut Paris Region, 2019) for the year 2003 (Figure  S1 ). Reference evapotranspiration (𝐸𝑇 0 ) for August was obtained from monthly modeled climatological data for the region, averaged for the 1985-2005 period (ALADIN model,  Stéfanon et al. , 2015). LULC parameter values for shade, crop evapotranspiration coefficient, albedo, and building intensity were assigned based on a combination of expert opinion and literature review (see Appendix B). Reference temperature data. In the Paris case study, we compared the InVEST model outputs with two datasets of land surface temperature (LST) and air temperature. LST maps with a 1-km horizontal resolution were retrieved from MODIS satellite products  (Wan, 2013).
The 1 st and 99 th percentiles were extracted from these air temperature data to define the extreme temperature conditions over the domain during the heat wave (to exclude visible outliers). The background reference rural temperature (Tref) was set to the 1 st percentile, while the maximum urban heat island intensity (UHImax) was the difference between both percentiles. Because the TEB/Surfex data were available for a 100 km x 100 km window, the rest of the analyses are presented for this domain, rather than the entire Ile-de-France region. The three datasets InVEST, TEB/Surfex and MODIS were resampled to a common 1-km grid for comparison, using bilinear interpolation.
Twin Cities Input data. We used 2016 LULC data from the National Land Cover Dataset  (NLCD; Homer et al. , 2020). Details on LULC parameters values are provided in Appendix B and in previous work  (Hamel et al. , 2021). Reference evapotranspiration for July 2016 was obtained from globally available data from the Consultative Group on International Agricultural Research  (Trabucco & Zomer, 2018). Reference temperature data. We compared air temperature as modeled by InVEST to two data sources: the midday LST maps of the study area during a regional heatwave on July 22, 2016, retrieved from Landsat data at a 30-m horizontal resolution for daytime and MODIS for nighttime  (Wan, 2013) ; and rasters of average summertime (June, July, August, 2011-2014) daytime and nighttime air temperatures across the study area, interpolated from a dense network of temperature sensors  (Smoliak et al.
, 2015). We projected the reference temperature data to match the InVEST output coordinate system and resampled all datasets to match the resolution (~1 km) of Smoliak et al. 's data using bilinear interpolation. The background reference rural temperature was defined using the average air temperature in July for the metropolitan area obtained from the US National Weather Service (23. 2 °C; NOAA 2020) and the maximum urban heat island intensity was taken from a global assessment of urban heat islands (2. 05°C; Chakraborty & Lee, 2019).
Model calibration and performance assessment For calibration, we further developed an optimization algorithm used in previous work  (Bosch et al. , 2021). The algorithm starts with default parameter values and implements a simulated annealing optimization to derive the values of the five parameters for daytime (rmix, dcool, Wa, We, Ws) or two parameters for nighttime (rmix, dcool,) until the algorithm converges to a solution that minimizes r 2 (or a limit number of 100 iterations is reached). The source code for the calibration tool can be found at: https://github. com/martibosch/invest-ucm-calibration/tree/v0. 6. 0 (and on Zenodo: Please refer to the Code Availability section) and a user guide available at: https://invest-ucm-calibration. readthedocs. io/en/latest/userguide. html. In addition, we performed a one-at-a-time sensitivity analyses to further understand how the calibration parameters influence model outputs.
Ranges of variation for each parameter are provided in Appendix C. To demonstrate the application of the InVEST model in practice and assess the model's ability to represent the effect of a change in LULC, we also examined the effect of a greening strategy on urban cooling in the first case study, where comparison data were available from an alternative model. We used the greening scenario LHV50 (50% of impervious areas covered by we estimated the proportion of available ground, as defined above, for each category of the LULC map, and computed the weighted average of initial parameter values and values for an urban forest. For example, for the "Parks or Gardens" LULC category, it was estimated that 15% of the ground was available, so a weighted average of the shade value for Forest (1) and original park value (0.