Kevin Marchesini
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View article: Accurate 3D Medical Image Segmentation with Mambas
Accurate 3D Medical Image Segmentation with Mambas Open
CNNs and Transformer-based architectures are recently dominating the field of 3D medical segmentation. While CNNs face limitations in the local receptive field, Transformers require significant memory and data, making them less suitable fo…
View article: Taming Mambas for 3D Medical Image Segmentation
Taming Mambas for 3D Medical Image Segmentation Open
Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with its distinctive strengths and limitations. CNNs are con…
View article: Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge
Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge Open
In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, result…
View article: Identifying Impurities in Liquids of Pharmaceutical Vials
Identifying Impurities in Liquids of Pharmaceutical Vials Open
View article: Investigating the ABCDE Rule in Convolutional Neural Networks
Investigating the ABCDE Rule in Convolutional Neural Networks Open
View article: Taming Mambas for Voxel Level 3D Medical Image Segmentation
Taming Mambas for Voxel Level 3D Medical Image Segmentation Open
Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with their distinctive strengths and limitations. CNNs are c…