Harnessing ResNet50 and SENet for enhanced ankle fracture identification.

BACKGROUND: Ankle fractures are prevalent injuries that necessitate precise diagnostic tools. Traditional diagnostic methods have limitations that can be addressed using machine learning techniques, with the potential to improve accuracy and expedite diagnoses. METHODS: We trained various deep learning architectures, notably the Adapted ResNet50 with SENet capabilities, to identify ankle fractures using a curated dataset of radiographic images. Model performance was evaluated using common metrics like accuracy, precision, and recall. Additionally, Grad-CAM visualizations were employed to interpret model decisions. RESULTS: The Adapted ResNet50 with SENet capabilities consistently outperformed other models, achieving an accuracy of 93%, AUC of 95%, and recall of 92%. Grad-CAM visualizations provided insights into areas of the radiographs that the model deemed significant in its decisions. CONCLUSIONS: The Adapted ResNet50 model enhanced with SENet capabilities demonstrated superior performance in detecting ankle fractures, offering a promising tool to complement traditional diagnostic methods. However, continuous refinement and expert validation are essential to ensure optimal application in clinical settings.

FEDM: a convolutional neural network based fertilised egg detection model.

1. The production of goose eggs holds significant economic value on a global scale and the quality of fertilised eggs is crucial for the successful hatching and sustained development of the poultry industry. Developing a low-cost fertilised egg identification system that is suitable for large-scale testing is of great significance. However, existing methods are expensive and have high environmental detection requirements, which limit their promotion.2. To address this issue, an improved object detection model called FEDM based on YOLOv5 is proposed, which has been shown to be outstanding among nine models. The main network of YOLOv5 is enhanced with the SENet attention mechanism to improve the feature selection capability. The C3_DCNv3 is introduced to enhance the detection ability of blood vessels in the fertilised eggs. The application of Dyhead significantly improved the representation capacity of the object detection head without any computational overhead. The loss function is replaced with MPDIoU to simplify the calculation process.3. Experimental results from the augmented dataset showed that the average precision of the FEDM reached 96.7%, which is a 5.5% improvement compared to the YOLOv5s model. FEDM exhibited better detection performance on eggs from different shooting angles than the YOLOv5 algorithm and achieves high detection speed.4. The FEDM secured significant advancement on the detection rate of the fourth day fertilised egg compared to the YOLOv5 algorithm. Based on this result, savings and space utilisation can be made, which has practical application value.

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.

PURPOSE: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network.

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

[Prediction Method of Elastic Modulus of Trabecular Bone Based on SE-DenseVoxNet].

Osteoporosis is one of the common metabolic diseases, which can easily lead to osteoporotic fractures. Accurate prediction of bone biomechanical properties is of great significance for the early prevention and diagnosis of osteoporosis. Bone mineral density measurement is currently used clinically as the gold standard for assessing bone strength and diagnosing osteoporosis, but studies have shown that bone mineral density can only explain 60% to 70% of bone strength changes, and trabecular bone microstructure is an important factor affecting bone strength. In order to establish the connection between trabecular bone microstructure and bone strength, this paper proposes a prediction method of trabecular bone modulus based on SE-DenseVoxNet. This method takes three-dimensional binary images of trabecular bone as input and predicts its elastic modulus in the z-axis direction. Experiments show that the error and bias between the predicted value of the method and the true value of the sample are small and have good consistency.

Enhanced YOLO v3 for precise detection of apparent damage on bridges amidst complex backgrounds.

A bridge disease identification approach based on an enhanced YOLO v3 algorithm is suggested to increase the accuracy of apparent disease detection of concrete bridges under complex backgrounds. First, the YOLO v3 network structure is enhanced to better accommodate the dense distribution and large variation of disease scale characteristics, and the detection layer incorporates the squeeze and excitation (SE) networks attention mechanism module and spatial pyramid pooling module to strengthen the semantic feature extraction ability. Secondly, CIoU with better localization ability is selected as the loss function for training. Finally, the K-means algorithm is used for anchor frame clustering on the bridge surface disease defects dataset. 1363 datasets containing exposed reinforcement, spalling, and water erosion damage of bridges are produced, and network training is done after manual labelling and data improvement in order to test the efficacy of the algorithm described in this paper. According to the trial results, the YOLO v3 model has enhanced more than the original model in terms of precision rate, recall rate, Average Precision (AP), and other indicators. Its overall mean Average Precision (mAP) value has also grown by 5.5%. With the RTX2080Ti graphics card, the detection frame rate increases to 84 Frames Per Second, enabling more precise and real-time bridge illness detection.

Surface defect detection of steel based on improved YOLOv5 algorithm.

To address the challenge of achieving a balance between efficiency and performance in steel surface defect detection, this paper presents a novel algorithm that enhances the YOLOv5 defect detection model. The enhancement process begins by employing the K-means++ algorithm to fine-tune the location of the prior anchor boxes, improving the matching process. Subsequently, the loss function is transitioned from generalized intersection over union (GIOU) to efficient intersection over union (EIOU) to mitigate the former's degeneration issues. To minimize information loss, Carafe upsampling replaces traditional upsampling techniques. Lastly, the squeeze and excitation networks (SE-Net) module is incorporated to augment the model's sensitivity to channel features. Experimental evaluations conducted on a public defect dataset reveal that the proposed method elevates the mean average precision (mAP) by seven percentage points compared to the original YOLOv5 model, achieving an mAP of 83.3%. Furthermore, our model's size is significantly reduced compared to other advanced algorithms, while maintaining a processing speed of 47 frames per second. This performance demonstrates the effectiveness of the proposed enhancements in improving both accuracy and efficiency in defect detection.

MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems.

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially.

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices.

Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage.

A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm.

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

Model design and parameter optimization of CNN for side-channel cryptanalysis.

BACKGROUND: The side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can effectively carry out cryptographic attacks. The CNNSCA network models that achieve cryptanalysis mainly include CNNSCA based on the VGG variant (VGG-CNNSCA) and CNNSCA based on the Alexnet variant (Alex-CNNSCA). The learning ability and cryptanalysis performance of these CNNSCA models are not optimal, and the trained model has low accuracy, too long training time, and takes up more computing resources. In order to improve the overall performance of CNNSCA, the paper will improve CNNSCA model design and hyperparameter optimization. METHODS: The paper first studied the CNN architecture composition in the SCA application scenario, and derives the calculation process of the CNN core algorithm for side-channel leakage of one-dimensional data. Secondly, a new basic model of CNNSCA was designed by comprehensively using the advantages of VGG-CNNSCA model classification and fitting efficiency and Alex-CNNSCA model occupying less computing resources, in order to better reduce the gradient dispersion problem of error back propagation in deep networks, the SE (Squeeze-and-Excitation) module is newly embedded in this basic model, this module is used for the first time in the CNNSCA model, which forms a new idea for the design of the CNNSCA model. Then apply this basic model to a known first-order masked dataset from the side-channel leak public database (ASCAD). In this application scenario, according to the model design rules and actual experimental results, exclude non-essential experimental parameters. Optimize the various hyperparameters of the basic model in the most objective experimental parameter interval to improve its cryptanalysis performance, which results in a hyper-parameter optimization scheme and a final benchmark for the determination of hyper-parameters. RESULTS: Finally, a new CNNSCA model optimized architecture for attacking unprotected encryption devices is obtained-CNNSCAnew. Through comparative experiments, CNNSCAnew's guessing entropy evaluation results converged to 61. From model training to successful recovery of the key, the total time spent was shortened to about 30 min, and we obtained better performance than other CNNSCA models.

GSEnet: feature extraction of gene expression data and its application to Leukemia classification.

Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module, and the SE-conv module. Effectiveness of the proposed model on the performance improvement of 9 representative classifiers is evaluated. Seven evaluation metrics are used for this assessment on the GSE99095 dataset. Robustness and advantages of the proposed model compared with representative feature selection methods are also discussed. Results show superiority of the proposed model on the improvement of the classification precision and accuracy.

SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination.

Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researchers have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by the cross-validation method of subject division. The results show that SparNet achieves a sensitivity of 95.07%, a specificity of 93.66%, and an accuracy of 94.37% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.

An automatic detection method of cerebral aneurysms in time-of-flight magnetic resonance angiography images based on attention 3D U-Net.

BACKGROUND: Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often leads to fatal consequences. However, if the aneurysm can be found and treated during asymptomatic periods, the probability of rupture can be greatly reduced. At present, time-of-flight magnetic resonance angiography is one of the most commonly used non-invasive screening techniques for cerebral aneurysm, and the application of deep learning technology in aneurysm detection can effectively improve the screening effect of aneurysm. Existing studies have found that three-dimensional features play an important role in aneurysm detection, but they require a large amount of training data and have problems such as a high number of FPs per case. METHODS: This paper proposed a novel method for aneurysm detection. First, a fully automatic cerebral artery segmentation algorithm without training data was used to extract the volume of interest, and then the 3D U-Net was improved by the 3D SENet module to establish an aneurysm detection model. Eventually a set of fully automated, end-to-end aneurysm detection methods have been formed. RESULTS: A total of 231 magnetic resonance angiography image data were used in this study, among which 132 were training sets, 34 were internal test sets and 65 were external test sets. The presented method obtained 97.89±0.88% sensitivity in the five-fold cross-validation and obtained 90.8% sensitivity with 2.47 FPs/case in the detection of the external test sets. CONCLUSIONS: Compared with the results of our previous studies and other studies, the method in this paper achieves the best sensitivity while maintaining low number of FPs per case. This result proves the feasibility, superiority, and further improvement potential of the improved method combining 3D U-Net and channel attention in the task of aneurysm detection.

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring.

BACKGROUND: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. RESULTS: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. CONCLUSIONS: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

Adaptively Dense Feature Pyramid Network for Object Detection.

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales. The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB). Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less. The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method. In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone. When testing with a lower resolution (300 × 300), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.

Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images.

BACKGROUND: Clinical histological grading of hepatocellular carcinoma (HCC) differentiation is of great significance in clinical diagnoses, treatments, and prognoses. However, it is challenging for radiologists to evaluate HCC gradings from medical images. PURPOSE: In this study, a novel deep neural network was developed by combining the squeeze-and-excitation networks (SENets) in a three-dimensional (3D) densely connected convolutional network (DenseNet), which is referred to as a 3D SE-DenseNet, for the classification of HCC grading using enhanced clinical magnetic resonance (MR) images obtained from two different clinical centers. METHOD: In the proposed architecture, the SENet was added as an additional layer between the dense blocks of the 3D DenseNet, to mitigate the impact of feature redundancy. For the HCC grading task, the 3D SE-DenseNet was trained after data augmentation, and it outperformed the 3D DenseNet based on the clinical dataset. RESULTS: The quantitative evaluations of the 3D SE-DenseNet on a two-class HCC grading task were conducted based on the dataset, which included 213 samples of the dynamic enhanced MR images. The proposed 3D SE-DenseNet demonstrated an accuracy of 83%, when compared with the 72% accuracy of the 3D DenseNet. CONCLUSION: Owing to the advantage of useful automatic feature learning by the SE layer, the 3D SE-DenseNet can simultaneously handle useful feature enhancement and superfluous feature suppression. The quantitative experiments confirm the excellent performance of the 3D SE-DenseNet in the evaluation of the HCC grading.

Prediction Method of Elastic Modulus of Trabecular Bone Based on SE-DenseVoxNet / 中国医疗器械杂志

Osteoporosis is one of the common metabolic diseases, which can easily lead to osteoporotic fractures. Accurate prediction of bone biomechanical properties is of great significance for the early prevention and diagnosis of osteoporosis. Bone mineral density measurement is currently used clinically as the gold standard for assessing bone strength and diagnosing osteoporosis, but studies have shown that bone mineral density can only explain 60% to 70% of bone strength changes, and trabecular bone microstructure is an important factor affecting bone strength. In order to establish the connection between trabecular bone microstructure and bone strength, this paper proposes a prediction method of trabecular bone modulus based on SE-DenseVoxNet. This method takes three-dimensional binary images of trabecular bone as input and predicts its elastic modulus in the z-axis direction. Experiments show that the error and bias between the predicted value of the method and the true value of the sample are small and have good consistency.

Memoria filogenética, evolución e historia en la primera psicología en la Argentina / Philogenetic memory, evolution, and history in the former psychology in Argentina

En el marco de una investigación más amplia, que indaga las diversas concepciones del tiempo, y las relaciones que se establecen entre el pasado y el presente en la explicación del desarrollo psíquico individual y colectivo, se analizará aquí específicamente: 1) el concepto de memoria filogenética usado en la psicología argentina de principios del siglo XX; 2) el papel que esta memoria no consciente cumplió en la explicación del desarrollo psicológico; y 3) su articulación con las nociones de evolución e historia en la interpretación psicosocial. Se sostiene aquí que la transformación de la memoria en objeto biológico en el marco de la teoría de la evolución, permitió una redefinición de la misma desde el punto de vista psicológico, que incluyó un modo particular de entender el carácter de representación inconsciente en la explicación del desarrollo psicológico en general, por parte de los autores de la época.

This paper analyses specifically 1) the use of the concept of philogenetic memory in the argentine psychology at the beginnings of twentieth century; 2) the role of this notion in the explications of psychological development; and 3) its relationships with the notions of evolution and history in the psychosocial interpretation. It is argued here that the transformation of the memory in a biological object from an evolution theory frame, allowed a redefinition of this notion from a psychological perspective. This change included a particular way of understanding the no conscious representation in the explication of the general psychological development, by the authors of this time.