dataesr/scientific-paragraphs-categorization huggingface dataset:
2D+t DL Methods Despite the success of 2D methods in producing accurate segmentations for individual echocardiographic frames, they often fail to maintain temporal consistency between frames  [8]. Prior to the publication of HMC-QU and TED, there were no publicly available datasets to train and compare methods that incorporate the temporal dimension in 2D+time echocardiography. This explains why so few papers have focused on this topic. Due to the lack of 2D+time annotated datasets, Wei et al. [11]  proposed a method to leverage the limited ED/ES annotated frames and propagate them to unannotated frames. This was achieved by training a 3D U-Net designed to predict both the deformation fields between each pair of consecutive frames and the segmentation masks at each frame of the sequence. The deformation fields are used to propagate the ED/ES reference annotations forward and backward in time through the sequence.
CARDINAL Dataset Acquisition Protocol: The proposed dataset consists of clinical examinations of 240 patients, acquired at the University Hospital of Lyon (Croix-Rousse Lyon Sud, France) under the regulation of the local ethics committee of the hospital. The complete dataset was acquired with GE ultrasound scanners. For each patient, 2D A4C and A2C view sequences were exported from the EchoPAC analysis software. Each exported sequence corresponds to a set of B-mode images expressed in polar coordinates. The same interpolation procedure as used for the CAMUS dataset was applied to express all sequences in Cartesian coordinates with a single grid resolution of 0. 31 mm 2. Each sequence in the CARDINAL dataset corresponds to a complete cardiac cycle defined as the interval between peaks of maximal LV cavity surface area.
Implemented DL Methods For a fair comparison, we implemented the 2D nnU-Net, U-Net LSTM, and 3D nnU-Net described in section 2 using the same Python library called ASCENT  6. These models shared the following training hyperparameters: batch size of 2, SGD optimizer with a learning rate of 0. 01 coupled with a polynomial decay scheduler, and 1000 training epochs. The 2D and 3D nnU-Net used a patch-wise approach to avoid resizing the images, thus preserving the native image resolution. To train the U-Net LSTM, the input images were resized to 256 × 256 and 24 consecutive frames were randomly selected and fed to the model to produce the corresponding segmentations. For inference, the sliding window approach with a Gaussian importance map was used. The prediction was given by the average of the softmax probabilities of all windows.
Results We evaluate the methods described in section 2 using three types of measures to get a complete picture of their performance in terms of segmentation accuracy (table  2 ), extraction of clinical indices (table  3 ) and temporal consistency (table 4). In each of these tables, we group the methods according to the datasets on which they were trained and tested (CARDINAL is abbreviated as CL and CAMUS is abbreviated as CS ) to make it easier to observe the change in performance when generalizing to a new dataset.
Geometric and Clinical Accuracy Table  2  shows the segmentation accuracy computed from the CARDINAL and CAMUS datasets for the 5 algorithms described in section 2. The values in bold correspond to the best scores for each metric for a given training/test dataset setup. From the results on the CARDINAL dataset (CL/CL case), we can see that the 3D nnU-Net has the best segmentation scores for all metrics, for both ED and ES. It is also interesting to note that the two temporal consistency methods (3D nnU-Net and U-Net LSTM) produce better results than the 2D nnU-Net method. This can be explained by the fact that the reference segmentation has regular properties along the temporal axis due to the annotation process. Methods that integrate the temporal dimension into their architecture are therefore more likely to produce segmentation results that are closer to the manual references.
It is worth mentioning that methods trained and tested on the same dataset (sections CL/CL and CS/CS in Table  2 ) get overall better results up to 1. 7x for the Hausdorff and MAD metrics. One reason for such an improvement is the larger amount of annotated training images for CARDINAL (18,793 im-ages from 190 training/validation patients, reference frames for the full cardiac cycle in A2C and A4C views) than for CAMUS (1,800 images from 450 training/validation patients, reference frames at ED and ES in A2C and A4C views). Table  3  reports the clinical metrics for the 5 methods. As in Table  2 , the methods enforcing temporal consistency gets the best results on CARDINAL, especially for the ejection fraction for which temporal consistency is essential (mean correlation score of 0. 917). Furthermore, the best models trained on CARDINAL or CAMUS produce similar results for volume estimation (average correlation of 0.
Indeed, the 3D nnU-Net temporal "inconsistencies" appear invisible to the expert eye. As a qualitative evaluation, fig. 1  illustrates in detail the temporal consistency of each of our own method on one patient from the CARDINAL test set. To complement this, we also provide in the supplementary material examples of temporally consistent and inconsistent segmentation results obtained by the 3D nnU-Net method for the CARDINAL and CAMUS datasets.
Generalization Across Datasets The ability to generalize across datasets is crucially important to gauge the capacity of a method to properly analyze data affected by a distributional shift. To this end, the models trained on CARDINAL were also evaluated on the CA-MUS test set without any fine-tuning. The results are reported in the "CL/CS" sections of tables 2 to 4. Among the methods evaluated, 3D nnU-Net is the undisputed best. It even produces competitive geometric and clinical scores compared with SOTA methods trained directly on CAMUS. Thanks to the integration of temporal consistency, the 3D nnU-Net trained on CARDINAL also produces one of the best correlation scores for the EF calculated on the CAMUS dataset, even when compared to SOTA methods trained directly on CAMUS. In view of these results, and considering that the annotation process between the two databases was not identical and was carried out by different experts (which inevitably introduces a bias during the learning phase), the generalization capacity of the 3D nnU-Net model seems remarkable.
Conclusion We evaluated the ability of different methods to accurately segment echocardiographic images, with a focus on temporal consistency and cross-dataset generalization. To this end, we introduced a new private database called CARDINAL, with annotations from an expert on the full cardiac cycle for each sequence. The results show that 3D nnU-Net and U-Net LSTM produce the best geometric and clinical scores on the CARDINAL dataset due to the integration of temporal persistence. Regarding the temporal consistency metrics, 3D nnU-Net performed significantly better than U-Net LSTM with only four sequences (instead of 98) out of 100 having at least one image that was temporally inconsistent. As far as cross-dataset generalization is concerned, 3D nnU-Net is also the best performing method.
Acknowledgment The authors gratefully acknowledge financial support of the  MEGA doctoral  school ( ED 162 ), the  NSERC   Canada Graduate Scholarships-Doctoral Program , the  FRQNT Doctoral Scholarships Program , and the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir " operated by the  French ANR. The authors thank  GENCI-IDRIS  for providing access to HPC ressources (Grant  2022-[AD010313603 ]).
Crop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to develop automated crop disease detection systems. However, centralized approach to data collection and model training induces challenges in terms of data privacy, availability, and transfer costs. To address these challenges, federated learning appears to be a promising solution. In this paper, we explored the application of federated learning for crop disease classification using image analysis. We developed and studied convolutional neural network (CNN) models and those based on attention mechanisms, in this case vision transformers (ViT), using federated learning, leveraging an open access image dataset from the "PlantVillage" platform.
As a result, they are considered sensitive by economic actors, and sharing them represents a competitive risk. By enabling several entities (growers) to collaborate or work together to train a model without sharing private data with a central server, federated learning guarantees data confidentiality and security, and also makes it possible to train models in remote or rural areas. Moreover, by minimizing sharing of personal data and giving users control over their data, federated learning reduces the risk of data leakage. It then ensures compliance with data privacy regulations, such as GDPR, and furthermore alleviates pressure on device resources. Each participant in the federated learning process ultimately benefits from a more robust and efficient model trained on a richer and wider range of data, regardless of the distribution and size of each participant's dataset  [11] ,  [12] ,  [13].
Despite the many advantages of federated learning, a number of challenges remain, including complexity of coordination, data heterogeneity, effective communication, and prevention of malicious attacks. In this paper, we evaluated and studied convolutional network models and models based on attention mechanisms (in particular Vision Transformers, ViT) using federated learning for disease detection. We have used four open source image datasets from the "PlantVillage" platform for our experiments. Our goal is to highlight the strengths of federated learning in crop disease classification, particularly with respect to user data security and enforcement of confidentiality of sensitive data. The article is structured as follows : Section 2 presents a state-of-the-art on crop disease detection and classification methods and federated learning.
Detection of crop diseases Crop diseases, considered as an abnormal condition affecting growth, yield and quality of a plant, have been the subject of many diagnostic and detection approaches. In recent years, methodologies based on machine learning and deep learning have been developed to detect plant diseases  [14] ,  [15] ,  [6] ,  [7] ,  [16]. CNNs methods are already used for plant leaf disease detection in the agricultural field; interested readers may find reviews in  [5] , and  [17]  for example. Deep learning techniques with convolutional neural networks (CNNs) have shown promising results with high accuracy for automating image processing, and leaf disease detection and prediction  [3] ,  [8] ,  [14] ,  [18]  when applied to image datasets from PlantVillage. Those works rely either on creating new architectures or on using pre-trained models.
To reduce the number of parameters, the authors use a depth-separable convolution. By testing the proposed model on three different categories of plant leaf datasets (PlantVillage for maize, potato and tomato, cassava leaves and rice), and comparing the performance of the proposed model with that of the pre-trained models (VGG-16, VGG-19, InceptionV3, ResNet50 and DenseNet201), it achieves higher accuracy of 99. 39%, 99. 66% and 76. 59% for the three categories of datasets, with reduced number of parameters. [3]  proposes a new approach based on a Deep CNN, using leaf images of 13 plant types from PlantVillage. Six types of data augmentation methods are employed to improve the performance of the model. The authors' proposed model achieves a disease classification accuracy of 96. 46%, superior to popular transfer learning approaches such as AlexNet, ResNet, VGG-16 and InceptionV3, but also to SVM, Decision Tree, Logistic Regression and K-NN models.
ViT has demonstrated impressive performance in recent years in many computer vision tasks, especially in leaf disease detection and classification  [19] ,  [6] ,  [20] ,  [7]. ViT offers the ability to process high resolution images with high levels of detail, learn global context, and be pre-trained on large amounts of data. In  [6] , the authors propose an approach based on combining a CNN with a ViT for plant disease classification. According to the results obtained in comparison with AlexNet and GoogleNet transfer learning approaches, attention blocks increase accuracy, but their combination with CNN blocks compensates for the speed of disease prediction. A new framework using heterogeneous data fusion and Transformer-like networks for late blight disease detection in vineyards is proposed in  [7].
Federated Learning (FL) Federated learning was introduced in  [21]  as a distributed machine learning approach. Its techniques aim to train algorithms on decentralized devices while keeping data private at the local level. This approach contrasts with traditional machine learning techniques that are centralized in nature, but also with techniques based on parallel computing. The latter are designed to optimize computation for machine learning on multiple processors using a centralized dataset. Although powerful, current machine learning techniques have data privacy shortcomings because they require sharing training data with a central server, which can pose privacy, security, and regulatory issues related to data collection and sharing  [12] ,  [13]. In federated learning, local participants (devices) train their local models based on their own private data.
The authors argue that the combination of these FL, AI and XAI techniques may be able to minimize several limitations and challenges in the healthcare system. In  [27] , the authors discuss and examine the application of federated learning in health informatics, specifically in the biomedical sector. They propose the connection of different sources of fragmented health data while preserving confidentiality, and thus improve the quality of care provided. In the article  [28] , the authors explore the application of federated learning (FL) in intelligent health systems, particularly in the context of remote patient health monitoring. After highlighting the importance of integrating federated learning into IoT-powered smart hospitals, the authors suggest using federated learning to address data privacy challenges by proposing to train machine learning models locally on hospital-installed IoT devices without transferring data to the cloud.
The authors explore and analyze the potential of federated learning to enable a wide range of IoT services, including IoT data sharing, attack detection, localization, mobile crowd detection, and IoT privacy and security. They then provide a broad survey of the use of federated learning in various key IoT applications such as smart health, smart transportation, drones, smart cities, and smart industry. Agricultural data, such as crop images, sensor data, or crop disease information, is often spread across a large number of devices and locations. Sharing it can create privacy and security risks for growers. In response to this challenge, federated learning appears to be a solution, although this paradigm is not yet widely used in this field and little work exists to date. For example, to address security and privacy issues, the authors of  [32]  propose a Privacy-Encoding-Based Federated Learning (PEFL) framework using a perturbation-based encoding technique and an LSTM auto-encoder to minimize the risk of data security and privacy violations in the context of smart farming.
Based on experimental results obtained from IoT data, they argue that the PEFL framework is able to effectively identify normal and attack patterns. In  [34] , the authors employ federated learning for corn disease prediction. Five CNN models were evaluated, followed by a comparative study considering the amount of network traffic and the number of parameters in each CNN. From the results, the AlexNet CNN model seems to be well suited for this approach, and the number of CNN parameters in the distributed learning paradigm is strongly correlated with the volume of data exchanged during the training process. In  [33] , a study of federated learning based on drones (or UAV: Unmanned Aerial Vehicle) for plant disease diagnosis, including classification of different types of insect pests, is conducted.
Federated learning framework Federated learning is a machine learning framework in which multiple workers or clients contribute to the training of a more robust and efficient global model that they will later share while keeping their data decentralized  [11]. Figure  1  shows an overview of the deployed FL architecture. We used open source datasets from the "PlantVillage" platform, and then simulate a collaborative set of clients who contribute to the improvement of a global model. This five-phase cycle is repeated until the predefined number of communication rounds between the central server and the individual clients participating in the process is reached. Thus, in each communication round, an evaluation of the overall model is performed by each client on its own test data. Once the final model is ready, it is distributed to all clients in the environment to perform inferences.
These different steps defines the behavior ot two main roles : the Client role (local workers) and the Server role (central federated server). Algorithms 1 and 2 describe more formally the behavior each of these two roles. for each client k in S t in parallel do 6: Algorithm 1 : Client Role 1: procedure UpdatesClient(k, W ) 2: B C ← (Split P k into batch of size B C ) 3: for each local epoch i from 1 to E C do 4: for each batch b in B C do 5: W ← W -L R ▽ ℓ(W, b W k t+1 ← ClientUpdate(k, W t ) 7: end for 8: W t+1 ← 1 n k k=1 n k * W k k+1 9: end for 10: end procedure In these two algorithms k denotes the index of C clients; B c denotes the size of the local batches; P k denotes the dataset partition for client k; E c denotes the number of iterations performed by the local models; W denotes the model weights; L R denotes the local learning rate, and R denotes the number of communication rounds between the central server and the clients.
• Visual Transformer (ViT): Introduced in 2021  [40] , ViT is a visual model based on the architecture of a transformer originally designed for natural language processing. Indeed, the transformer is a deep learning model using attention mechanisms. Composed of several layers of self-attention, a pure transformer applied directly to image patch sequences can work exceptionally accurately on image classification tasks. The ViT model represents an image as a series of patch embeddings and directly predicts class labels for the image. Compared to CNNs, ViT has a generally lower inductive bias, resulting in an increased dependence on model regularization or data augmentation when trained on smaller datasets. When pre-trained on a sufficient amount of data, ViTs exhibit extraordinary performance, at least as robust as their ResNet counterparts, but with fewer computational resources for pre-training.
Data description For training and validation of the FL architecture, we used publicly available data from the PlantVillage database. This contains over 50,000 images of healthy and infected plant leaves, divided into 38 categories according to species and diseases. It is a repository of images on plant health, freely accessible and allowing the development of disease diagnostics. In our case, we used data from four types of plants for our different experiments: grapes, apples, corn, and tomatoes. These data are classified according to different categories of healthy and infected leaves. A description of the latter is provided in Table  1. Below are some images of the leaves corresponding to the different classes of each of the four plants. Figure  2  shows images of apple leaves, Figure  3  shows images of corn leaves, Figure  4  shows images of grape leaves, and Figure  5  shows images of tomato leaves.
Experimental setup The experiments were carried out using Python 3. 9. 12, on a computer equipped with a 3. 60 GHz Intel Xeon W-2223 processor and an NVIDIA TU104GL Quadro RTX 5000 graphics card, running Ubuntu 20. 04. 4 LTS 64 bits. We used Pytorch and Keras libraries for developing Deep Learning architectures, as well as cuDNN 8. 2. 1 and the CUDA 11. 2. 67 toolkit for deep learning on the GPU. We ran the experiments on four datasets, varying the parameters defined in Table  2  for each of them.
Presentation of results In this section, we present the results of different tests of the models for the different parameters, consisting in the variation of the number of clients participating in the federated learning process to train the models, the variation in the number of communication rounds between the clients and the server, the variation in the number of epochs at the local level for each client, and finally a comparative study of the performance of the models according to the four datasets of the PlantVillage platform selected for experiments. We evaluate the performance of the selected models with respect to four specific aspects : • The influence of the number of clients, • The influence of the number of communication rounds between clients and the server, • The influence of the number of iterations or epochs locally for each client, • The comparison of the performances with respect to the different datasets.
5%. The MobileNetV2, Vgg16, and DenseNet121 models also perform well, but slightly worse than ResNet50, with Accuracy and F1-Score averages close to 98%, 97. 7%, and 97. 5%, respectively, for the different client configurations. The Vision Transformer architecture variants ViT B16 and ViT B32 underperforms than ResNet50, MobileNetV2, Vgg16, and DenseNet121, with Accuracy and F1-Score averages of 95. 9% and 94. 5 respectively for ViT B16 and ViT B32 for different client configurations. The InceptionV3 yields poorer results compared to other models, with an Accuracy and F1-Score of about 92% for 3 clients. Based on the data presented in Table  3 , along with the graphical representations in Figures  6  and 7  depicting Accuracy and F1-Score respectively, it can be deduced that the ResNet50 architecture, despite its complexity, delivers remarkable results.
This decrease can be explained by the fact that, while still having the same amount of data overall, increasing the number of clients leads to a decrease in the amount of data for each client, and thus the model has less data to train compared to the 3-client configuration. Furthermore, unlike the other models, figures 8 and 9 show that the performance of the InceptionV3 model decreases significantly as the number of clients increases. From this experiment, we find that overall and from the results obtained, the FL model training process is impacted by the number of clients involved, although the magnitude of this impact varies with each model's architecture. The ResNet50, MobileNetV2, Vgg16, and DenseNet121 models tend to see a decrease in performance as the number of clients increases, although this decrease is relatively small for ResNet50 and MobileNetV2.