89 research outputs found

    Finite element analysis of porously punched prosthetic short stem virtually designed for simulative uncemented hip arthroplasty

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    Background: There is no universal hip implant suitably fills all femoral types, whether prostheses of porous short-stem suitable for Hip Arthroplasty is to be measured scientifically. Methods: Ten specimens of femurs scanned by CT were input onto Mimics to rebuild 3D models; their *stl format dataset were imported into Geomagic-Studio for simulative osteotomy; the generated *.igs dataset were interacted by UG to fit solid models; the prosthesis were obtained by the same way from patients, and bored by punching bears designed by Pro-E virtually; cements between femora and prosthesis were extracted by deleting prosthesis; in HyperMesh, all compartments were assembled onto four artificial joint style as: (a) cemented long-stem prosthesis; (b) porous long-stem prosthesis; (c) cemented short-stem prosthesis; (d) porous short-stem prosthesis. Then, these numerical models of Finite Element Analysis were exported to AnSys for numerical solution. Results: Observed whatever from femur or prosthesis or combinational femora-prostheses, “Kruskal-Wallis” value p > 0.05 demonstrates that displacement of (d) ≈ (a) ≈ (b) ≈ (c) shows nothing different significantly by comparison with 600 N load. If stresses are tested upon prosthesis, (d) ≈ (a) ≈ (b) ≈ (c) is also displayed; if upon femora, (d) ≈ (a) ≈ (b) < (c) is suggested; if upon integral joint, (d) ≈ (a) < (b) < (c) is presented. Conclusions: Mechanically, these four sorts of artificial joint replacement are stabilized in quantity. Cemented short-stem prostheses present the biggest stress, while porous short-stem & cemented long-stem designs are equivalently better than porous long-stem prostheses and alternatives for femoral-head replacement. The preferred design of those two depends on clinical conditions. The cemented long-stem is favorable for inactive elders with osteoporosis, and porously punched cementless short-stem design is suitable for patients with osteoporosis, while the porously punched cementless short-stem is favorable for those with a cement allergy. Clinically, the strength of this study is to enable preoperative strategy to provide acute correction and decrease procedure time

    FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation

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    Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However, an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the pre-trained model. This results in the subject failing to comprehensively inherit the attributes in its category, causing poor attribute-related generations. In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category. This modeling enables the subject to inherit public attributes from its category while learning its private attributes from the user-provided example. Specifically, we propose a plug-and-play method, Subject-Derived regularization (SuDe). It constructs the base-derived class modeling by constraining the subject-driven generated images to semantically belong to the subject's category. Extensive experiments under three baselines and two backbones on various subjects show that our SuDe enables imaginative attribute-related generations while maintaining subject fidelity. Codes will be open sourced soon at FaceChain (https://github.com/modelscope/facechain).Comment: accepted by CVPR202

    Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

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    Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the label candidates, which could be avoidable since the contradiction with image-level class tags is easy to be detected. In this paper, we develop a group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion. Firstly, we adaptively split the semantic categories into In-Candidate (IC) and OC groups for each OC pixel according to their prior annotation correlation and posterior prediction correlation. Then, we derive a differentiable rectification loss to force OC pixels to shift to the IC group. Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM, MCTformer), we can achieve remarkable performance gains on both Pascal VOC (+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with negligible extra training overhead, which justifies the effectiveness and generality of our OCR.Comment: Accepted to CVPR202
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