Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attent...
详细信息
Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].
Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated ...
详细信息
Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi
This work investigates the temperature-dependent capacitance-voltage (C-V) characteristics of NiO/Ga2O3 heterojunction diodes (HJDs) before and after thermal annealing. The voltage barrier (VB) and the effective dopin...
详细信息
The microstructural and electrical-properties of a prepared Ni/Cr/Ni/FeO/n-GaN metal/oxide/semiconductor (MOS) heterojunction (HJ) (as-deposited and after 600 oC annealing) with biologically synthesized iron oxide (Fe...
详细信息
Two-dimensional van der Waals (vdW) heterostructures possess electrical and optoelectronic properties that offer a promising framework for the development of advanced nanoscale electronic devices. To achieve top-notch...
详细信息
This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consist...
详细信息
Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both sema...
详细信息
computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-...
详细信息
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challen...
详细信息
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a
作者：
Srinivas GandlaJinsik YoonCheol-Woong YangHyungJune LeeWook ParkSunkook KimMultifunctional Nano Bio Electronics Lab
Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Institute for Wearable Convergence Electronics
Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. Electron Microscopy Research Laboratory
Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Intelligent Networked Systems Lab
Department of Computer Science and Engineering Ewha Womans University Ewhayeodae-gil Seodaemun-gu Seoul 03760 Republic of Korea. Institute for Wearable Convergence Electronics
Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. parkwook@khu.ac.kr. Multifunctional Nano Bio Electronics Lab
Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. seonkuk@skku.edu.
暂无评论