Connectivity Architecture Research Articles

An attention mechanism-based lightweight UNet for musculoskeletal ultrasound image segmentation.

Accurate musculoseletal ultrasound (MSKUS) image segmentation is crucial for diagnosis and treatment planning. Compared with traditional segmentation methods, deploying deep learning segmentation methods that balance segmentation efficiency, accuracy, and model size on edge devices has greateradvantages. This paper aims to design a MSKUS image segmentation method that has fewer parameters, lower computation complexity and higher segmentationaccuracy. In this study, an attention mechanism-based lightweight UNet (AML-UNet) is designed to segment target muscle regions in MSKUS images. To suppress the transmission of redundant feature, Channel Reconstruction and Spatial Attention Module is designed in the encoding path. In addition, considering the inherent characteristic of MSKUS image, Multiscale Aggregation Module is developed to replace the skip connection architecture of U-Net. Deep supervision is also introduced to the decoding path to refine predicted masks gradually. Our method is evaluated on two MSKUS 2D-image segmentation datasets, including 3917 MSKUS and 1534 images respectively. In the experiments, a five-fold cross-validation method is adopted in ablation experiments and comparison experiments. In addition, Wilcoxon Signed-Rank Test and Bonferroni correction are employed to validate the significance level. 0.01 was used as the statistical significance level in ourpaper. AML-UNet yielded a mIoU of 84.17% and 90.14% on two datasets, representing a 3.38% ( ) and 3.48% ( ) over the Unext model. The number of parameters and FLOPs are only 0.21M and 0.96G, which are 1/34 and 1/29 of those in comparison withUNet. Our proposed model achieved superior results with fewer parameters while maintaining segmentation efficiency and accuracy compared to othermethods.

Microsoft applications of 6G in V2X

Abstract. With the rapid advancement of intelligent transportation systems and autonomous driving technologies, 6G networks, as a key technology for future communications, are set to bring trans-formative changes to V2X (Vehicle-to-Everything) communication. This paper explores the application prospects of 6G in V2X, with a focus on the critical technological support 6G provides to vehicular networks. The high transmission rates and ultra-low latency of 6G will significantly enhance real-time interactions between vehicles and between vehicles and infrastructure, improving traffic safety and efficiency. Additionally, 6G's broad connectivity and flexible network architecture will support a greater number of simultaneous device connections, facilitating the widespread adoption of intelligent transportation systems. However, 6G faces challenges in V2X applications, including security issues, privacy protection, and internet power supply concerns. The paper analyzes these challenges, proposes corresponding solutions, and envisions the future development of 6G technology in the V2X domain.

Development of the whole-brain functional connectome explored via graph theory analysis

Introduction Adolescence is a period of remarkable development as children’s brains change to resemble adult brains. Resting state fMRI measures fluctuations in blood-oxygen signal from which we can infer functional connectivity (FC). Graph theory is a branch of mathematics that can quantify the complex patterns of connectivity and network architecture inherent in the functional connectome. An ideal graph theory analysis explores edges that are weighted, directional, and heterogenous (can be positive or negative). Recent developmental studies have applied graph theory to the functional connectome, yet due to the considerable complexity added by each facet, most ignore one or more aspects of an ideal graph theory analysis (directionality and heterogeneity). Methods The present cross-sectional study measured FC in typically developing children, adolescents, and young adults (age 6-24 years) using 150+ echo-planar volumes (3.6mm isotropic voxels, repetition/echo time=2000/30ms) acquired at rest. A standard pre-processing pipeline was used, and the functional connectome was quantified using a weighted, directed graph analysis, including both positive and negative connections. Five different graph theory metrics were utilized to quantify developmental trajectories: connection density, modularity, clustering coefficient, global efficiency, and betweenness centrality. Positive and negative connections were analyzed separately, and age and sex associations were explored. Results The total sample comprised 219 participants (mean age (SD) [range] = 14.1 (3.3) [6.5-24.0] years, 50% female). For positive connections, modularity and betweenness centrality increased with age (both p<0.001), while connection density, clustering coefficient, and global efficiency decreased with age (all p<0.001). By contrast, for negative connections, modularity and betweenness centrality decreased with age (p=0.002, p=0.003), while connection density, clustering coefficient , and global efficiency increased with age (p<0.001, p<0.001, p=0.003). Effects of sex, hemisphere, and their interaction were minimal, though global efficiency for negative connections was higher in the right hemisphere than the left (p<0.001). Conclusion Graph theory appears to be a useful tool for quantifying the complex development of the functional connectome. The developmental changes presented here may be driven by an intrinsic pressure to balance functionality with low metabolic cost to maintain the network. The positive connection network appears to shift towards a more efficient conformation resembling “small-world” architecture. In contrast, the negative connection network seems to shift away from such efficient architecture, possibly to prioritize improving functionality before later refinement.

Local molecular and connectomic contributions of tau-related neurodegeneration.

Neurodegeneration in Alzheimer's disease (AD) is known to be mostly driven by tau neurofibrillary tangles. However, both tau and neurodegeneration exhibit variability in their distribution across the brain and among individuals, and the relationship between tau and neurodegeneration might be influenced by several factors. I aimed to map local molecular and connectivity characteristics that affect the association between tau pathology and neurodegeneration. The current study was conducted on the cross-sectional tau-PET and longitudinal T1-weighted MRI scan data of 186 participants from the ADNI dataset including 71 cognitively unimpaired (CU) and 115 mild cognitive impairment (MCI) individuals. Furthermore, the normative molecular profile of a region was defined using neurotransmitter receptor densities, gene expression, T1w/T2w ratio (myelination), FDG-PET (glycolytic index, glucose metabolism, and oxygen metabolism), and synaptic density. I found that the excitatory-inhibitory (E:I) ratio, myelination, synaptic density, glycolytic index, and functional connectivity are linked with deviation in the relationship between tau and neurodegeneration. Furthermore, there was spatial similarity between tau pathology and glycolytic index, synaptic density, and functional connectivity across brain regions. The current study demonstrates that the regional susceptibility to tau-related neurodegeneration is associated with specific molecular and connectomic characteristics of the affected neural systems. I found that the molecular and connectivity architecture of the human brain is linked to the different effects of tau pathology on downstream neurodegeneration.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Self-adaptive rotational electromagnetic energy generation as an alternative to triboelectric and piezoelectric transductions

Triboelectric and piezoelectric energy harvesters can hardly power most microelectronic systems. Rotational electromagnetic harvesters are very promising alternatives, but their performance is highly dependent on the varying mechanical sources. This study presents an innovative approach to significantly increase the performance of rotational harvesters, based on dynamic coil switching strategies for optimization of the coil connection architecture during energy generation. Both analytical and experimental validations of the concept of self-adaptive rotational harvester were carried out. The adaptive harvester was able to provide an average power increase of 63.3% and 79.5% when compared to a non-adaptive 16-coil harvester for harmonic translation and harmonic swaying excitations, respectively, and 83.5% and 87.2% when compared to a non-adaptive 8-coil harvester. The estimated energy conversion efficiency was also enhanced from ~80% to 90%. This study unravels an emerging technological approach to power a wide range of applications that cannot be powered by other vibrationally driven harvesters.

Direct Retrieval of Orthographic Representations in Chinese Handwritten Production: Evidence from a Dynamic Causal Modeling Study.

This present study identified an optimal model representing the relationship between orthography and phonology in Chinese handwritten production using dynamic causal modeling, and further explored how this model was modulated by word frequency and syllable frequency. Each model contained five volumes of interest in the left hemisphere (angular gyrus [AG], inferior frontal gyrus [IFG], middle frontal gyrus [MFG], superior frontal gyrus [SFG], and supramarginal gyrus [SMG]), with the IFG as the driven input area. Results showed the superiority of a model in which both the MFG and the AG connected with the IFG, supporting the orthography autonomy hypothesis. Word frequency modulated the AG → SFG connection (information flow from the orthographic lexicon to the orthographic buffer), and syllable frequency affected the IFG → MFG connection (information transmission from the semantic system to the phonological lexicon). This study thus provides new insights into the connectivity architecture of neural substrates involved in writing.

Common functional mechanisms underlying dynamic brain network changes across five general anesthetics: A rat fMRI study.

Reversible loss of consciousness is the primary therapeutic endpoint of general anesthesia; however, the drug-invariant mechanisms underlying anesthetic-induced unconsciousness are still unclear. This study aimed to investigate the static, dynamic, topological and organizational changes in functional brain network induced by five clinically-used general anesthetics in the rat brain. Male Sprague-Dawley rats (n = 57) were randomly allocated to received propofol, isoflurane, ketamine, dexmedetomidine, or combined isoflurane plus dexmedetomidine anesthesia. Resting-state functional magnetic resonance images were acquired under general anesthesia and analyzed for changes in dynamic functional brain networks compared to the awake state. Different general anesthetics induced distinct patterns of functional connectivity inhibition within brain-wide networks, resulting in multi-level network reorganization primarily by impairing the functional connectivity of cortico-subcortical networks as well as by reducing information transmission capacity, intrinsic connectivity, and network architecture stability of subcortical regions. Conversely, functional connectivity and topological properties were preserved within cortico-cortical networks, albeit with fewer dynamic fluctuations under general anesthesia. Our findings highlighted the effects of different general anesthetics on functional brain network reorganization, which might shed light on the drug-invariant mechanism of anesthetic-induced unconsciousness.

Single entanglement connection architecture between multi-layer bipartite hardware efficient ansatz

Variational quantum algorithms are among the most promising algorithms to achieve quantum advantages in the noisy intermediate-scale quantum (NISQ) era. One important challenge in implementing such algorithms is to construct an effective parameterized quantum circuit (also called an ansatz). In this work, we propose a single entanglement connection architecture (SECA) for a bipartite hardware efficient ansatz (HEA) by balancing its expressibility, entangling capability, and trainability. Numerical simulations with a one-dimensional Heisenberg model and quadratic unconstrained binary optimization (QUBO) issues were conducted. Our results indicate the superiority of SECA over the common full entanglement connection architecture in terms of computational performance. Furthermore, combining SECA with gate-cutting technology to construct distributed quantum computation (DQC) can efficiently expand the size of NISQ devices under low overhead. We also demonstrated the effectiveness and scalability of the DQC scheme. Our study is a useful indication for understanding the characteristics associated with an effective training circuit.

Evolution of connectivity architecture in the Drosophila mushroom body

Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits change at the cellular level over evolutionary time remains largely unanswered. Here, using a phylogenetically informed framework, we compare the olfactory circuits of three closely related Drosophila species that differ in their chemical ecology: the generalists Drosophila melanogaster and Drosophila simulans and Drosophila sechellia that specializes on ripe noni fruit. We examine a central part of the olfactory circuit that, to our knowledge, has not been investigated in these species—the connections between projection neurons and the Kenyon cells of the mushroom body—and identify species-specific connectivity patterns. We found that neurons encoding food odors connect more frequently with Kenyon cells, giving rise to species-specific biases in connectivity. These species-specific connectivity differences reflect two distinct neuronal phenotypes: in the number of projection neurons or in the number of presynaptic boutons formed by individual projection neurons. Finally, behavioral analyses suggest that such increased connectivity enhances learning performance in an associative task. Our study shows how fine-grained aspects of connectivity architecture in an associative brain center can change during evolution to reflect the chemical ecology of a species.

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro.

Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice.

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within 'remapped' forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within ‘remapped’ forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

The VAMP-associated protein VAP27-1 plays a crucial role in plant resistance to ER stress by modulating ER–PM contact architecture in Arabidopsis

The endoplasmic reticulum (ER) and the plasma membrane (PM) form ER–PM contact sites (EPCSs) that allow the ER and PM to exchange materials and information. Stress-induced disruption of protein folding triggers ER stress, and the cell initiates the unfolded protein response (UPR) to resist the stress. However, whether EPCSs play a role in ER stress in plants remains unclear. VESICLE-ASSOCIATED MEMBRANE PROTEIN (VAMP)-ASSOCIATED PROTEIN 27-1 (VAP27-1) functions in EPCS tethering and is encoded by a family of 10 genes (VAP27-1–10) in Arabidopsis thaliana. Here, we used CRISPR-Cas9-mediated genome editing to obtain a homozygous vap27-1 vap27-3 vap27-4 (vap27-1/3/4) triple mutant lacking three of the key VAP27 family members in Arabidopsis. The vap27-1/3/4 mutant exhibits defects in ER–PM connectivity and EPCS architecture, as well as excessive UPR signaling. We further showed that relocation of VAP27-1 to the PM mediates specific VAP27-1-related EPCS remodeling and expansion under ER stress. Moreover, the spatiotemporal dynamics of VAP27-1 at the PM increase ER–PM connectivity and enhance Arabidopsis resistance to ER stress. In addition, we revealed an important role for intracellular calcium homeostasis in the regulation of UPR signaling. Taken together, these results broaden our understanding of the molecular and cellular mechanisms of ER stress and UPR signaling in plants, providing additional clues for improving plant broad-spectrum resistance to different stresses.

The methodology of using ontology when designing a system for evaluating the economic efficiency of geological exploration

The use of the ontological approach in the design of various information systems quite often aims at finding hidden dependencies that are not obvious at the stage of a system design. Another important aspect of the application of ontology is the need to take into account a large number of evaluation factors. Often there is a situation when the use of a standard binary architecture of connections between system elements, by analogy with databases, does not give the necessary result. This is usually due to the limitations of such relationships (one to one, one to many) when designing an information system. Accordingly, it is the ontological approach that allows us to resolve this contradiction and take into account those relationships and factors that could not be taken into account in the binary data structure. The purpose of this work is to form methodological recommendations on the use of ontology in the process of designing a system for evaluating the economic efficiency of geological exploration. As a result, we propose the structure of the ontological model, in relation to the block of parameters for assessing the economic efficiency of exploration, as well as a variant of applying this structure in practice. In the future, it is planned to develop an information system for evaluating the effectiveness of geological exploration based on the use of this ontological model.

Token singularity understanding and removal for transformers

This work delves into unveiling the singularity issue latent in global attention-based Transformers. Empirical and theoretical analyses elucidate that interrelationships among token channels lead to singularities, impeding the training of attention weights. Concretely, the similar neighbor pixels within image patches can form intercorrelated channels after being flattened. Images that one color dominates can possess correlated channels. Furthermore, the fixed global connection architecture retains correlation relationships, contributing to the persistence of singularities. High singularity risks reducing Transformers’ performance and robustness. Based on the singularity analysis, we propose the Token Singularity Removal (TSR) strategy. It incorporates the Dual-Tree Complex Wavelet Transform (DTCWT) stem and Feature Decorrelation (FD) loss, aiming to encourage Transformers to learn tokens with unrelated channels and eliminate singularities. Experimental validation across various image classification datasets and corruption image data sets demonstrate improved accuracy and robustness of Transformers utilizing the TSR strategy. Our code is publicly available at https://github.com/wdanc/TSR.

The neurobiological effects of childhood maltreatment on brain structure, function, and attachment.

Childhood maltreatment is a risk factor for psychopathologies, and influences brain development at specific periods, particularly during early childhood and adolescence. This narrative review addresses phenotypic alterations in sensory systems associated with specific types of childhood maltreatment exposure, periods of vulnerability to the neurobiological effects of maltreatment, and the relationships between childhood maltreatment and brain structure, function, connectivity, and network architecture; psychopathology; and resilience. It also addresses neurobiological alterations associated with maternal communication and attachment disturbances, and uses laboratory-based measures during infancy and case-control studies to elucidate neurobiological alterations in reactive attachment disorders in children with maltreatment histories. Moreover, we review studies on the acute effects of oxytocin on reactive attachment disorder and maltreatment and methylation of oxytocin regulatory genes. Epigenetic changes may play a critical role in initiating or producing the atypical structural and functional brain alterations associated with childhood maltreatment. However, these changes could be reversed through psychological and pharmacological interventions, and by anticipating or preventing the emergence of brain alterations and subsequent psychopathological risks.

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

Network architecture of intrinsic connectivity in a mammalian spinal cord (the central nervous system’s caudal sector)

The vertebrate spinal cord (SP) is the long, thin extension of the brain forming the central nervous system's caudal sector. Functionally, the SP directly mediates motor and somatic sensory interactions with most parts of the body except the face, and it is the preferred model for analyzing relatively simple reflex behaviors. Here, we analyze the organization of axonal connections between the 50 gray matter regions forming the bilaterally symmetric rat SP. The assembled dataset suggests that there are about 385 of a possible 2,450 connections between the 50 regions for a connection density of 15.7%. Multiresolution consensus cluster analysis reveals a hierarchy of structure-function subsystems in this neural network, with 4 subsystems at the top level and 12 at the bottom-level. The top-level subsystems include a) a bilateral subsystem related most clearly to somatic and autonomic motor functions and centered in the ventral horn and intermediate zone; b) a bilateral subsystem associated with general somatosensory functions and centered in the base, neck, and head of the dorsal horn; and c) a pair of unilateral, bilaterally symmetric subsystems associated with nociceptive information processing and occupying the apex of the dorsal horn. The intrinsic SP network displayed no hubs, rich club, or small-world attributes, which are common measures of global functionality. Advantages and limitations of our methodology are discussed in some detail. The present work is part of a comprehensive project to assemble and analyze the neurome of a mammalian nervous system and its interactions with the body.

EDDS-Unet: An Encoder-Decoder Double Skip Connection Scheme for Skin Lesion Segmentation

The paper presents an approach for effective skin lesion segmentation from dermatoscopic images. Aiming at transferring the weights trained from a network originally designed for image classification task, this study proposes to utilize the first layers of EfficientNet as the encoding layers of a U-Net based architecture. Besides, we introduce an encoder-decoder double skip connection scheme, a new skip connection architecture for extracting useful spatial details of skin lesions from the encoding layers. By the double skip scheme, the approach not only fuses information from the encounter layer in the encoder path to the corresponding layer in the decoder path, but also takes into account information of the proceeding encoding layer. In addition, we propose a new decoder network using the Residual blocks and Convolutional Block Attention Module (CBAM) blocks to handle the gradient vanishing problem as well as penalize the weight of each layer. The proposed Encoder-Decoder Double Skip with the Unet architecture, namely EDDS-Unet, has shown promising performance when evaluated on the official ISIC 2017 challenge and the PH2 databases. The proposed method achieves high evaluation scores with the Dice Similarity Coefficients of 0.907 for the ISIC 2017 and 0.950 for the PH2 databases without pre-or post-processing steps.