28 research outputs found
Advances in Craniofacial Surgery
Calvaria development initiates by growth from primary ossification centers meeting each other to form suture sites. The term craniosynostosis describes premature fusion of one or more of the calvarial sutures. Deformities are usually observable during the first few months of the newborn’s life. The premature fusion of sutures could produce intracranial pressure elevation and consequently lead to abnormal neurocognitive = neurologic development. Patients with craniosynostosis require surgical plans containing multiple surgical staging. In the following chapter, we present our experience in surgical treatment of children with various craniosynostosis syndromes
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
Development of advance surface Electromyogram (sEMG)-based Human-Machine
Interface (HMI) systems is of paramount importance to pave the way towards
emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the
main focus of recent literature was on development of different Deep Neural
Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR)
at a macroscopic level (i.e., directly from sEMG signals). At the same time,
advancements in acquisition of High-Density sEMG signals (HD-sEMG) have
resulted in a surge of significant interest on sEMG decomposition techniques to
extract microscopic neural drive information. However, due to complexities of
sEMG decomposition and added computational overhead, HGR at microscopic level
is less explored than its aforementioned DNN-based counterparts. In this
regard, we propose the HYDRA-HGR framework, which is a hybrid model that
simultaneously extracts a set of temporal and spatial features through its two
independent Vision Transformer (ViT)-based parallel architectures (the so
called Macro and Micro paths). The Macro Path is trained directly on the
pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p
values of the extracted Motor Unit Action Potentials (MUAPs) of each source.
Extracted features at macroscopic and microscopic levels are then coupled via a
Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR
framework through a recently released HD-sEMG dataset, and show that it
significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR
framework achieves average accuracy of 94.86% for the 250 ms window size, which
is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively
Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction
The paper proposes a novel hybrid discovery Radiomics framework that
simultaneously integrates temporal and spatial features extracted from non-thin
chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC)
malignancy with minimum expert involvement. Lung cancer is the leading cause of
mortality from cancer worldwide and has various histologic types, among which
LUAC has recently been the most prevalent. LUACs are classified as
pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and
accurate knowledge of the lung nodules malignancy leads to a proper treatment
plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT
scan is the primary imaging modality to assess and predict the invasiveness of
LUACs. However, the radiologists' analysis based on CT images is subjective and
suffers from a low accuracy compared to the ground truth pathological reviews
provided after surgical resections. The proposed hybrid framework, referred to
as the CAET-SWin, consists of two parallel paths: (i) The Convolutional
Auto-Encoder (CAE) Transformer path that extracts and captures informative
features related to inter-slice relations via a modified Transformer
architecture, and; (ii) The Shifted Window (SWin) Transformer path, which is a
hierarchical vision transformer that extracts nodules' related spatial features
from a volumetric CT scan. Extracted temporal (from the CAET-path) and spatial
(from the Swin path) are then fused through a fusion path to classify LUACs.
Experimental results on our in-house dataset of 114 pathologically proven
Sub-Solid Nodules (SSNs) demonstrate that the CAET-SWin significantly improves
reliability of the invasiveness prediction task while achieving an accuracy of
82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-fold
cross-validation.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0872
Benign Cementoblastoma Involving Deciduous and Permanent Mandibular Molars: A Case Report
Cementoblastomas are rare benign odontogenic tumors. Diagnosis of these lesions must be made by an association of clinical, radiographic, and histopathological findings. Cementoblastomas rarely occur in both primary and permanent dentitions. We describe the sixth case of cementoblastoma in the literature with the involvement of both deciduous and permanent teeth. The aim of this case report is to present the clinicoradiopathologic features of a cementoblastoma in a 4.5-year-old boy with an unusual recurrence. The first clinical and radiographic features appeared on the deciduous mandibular second molar. The second lesion occurred 1 year after treatment at 5.5 years old, involving the permanent mandibular first molar, and a subsequent lesion was seen at age 8 years in the edentulous region of the extracted mandibular first molar. After the last surgery, there was no recurrence of the lesion at 6 months’ follow-up. Follow-ups of patients with cementoblastomas are highly recommended for an early detection of recurrence