History and Genetics of Retinoblastoma

The history of retinoblastoma (RB) goes back to 1597 when Pieter Pawius of Amsterdam described a tumor that resembled retinoblastoma. “Fungus haematodes” was the first term used to describe retinoblastoma. Later, the American Ophthalmological Society approved the term retinoblastoma in 1926. The retinoblastoma protein is encoded by the RB1 gene located at 13q14. The functioning model of the tumor suppressor genes was first proposed by Alfred Knudson in the 1970s who precisely explained the hereditary mechanism of retinoblastoma. If both alleles of this gene are mutated, the protein is inactivated and this results in the development of retinoblastoma. One mutation can be either germline or somatic and the second one is always somatic. Differentiation between sporadic and germline retinoblastoma variants requires the identification of the RB1 germline status of the patient. This identification is important for assessing the risk of additional tumors in the same eye, the other eye, and the risk of secondary tumors. Thus, genetic testing is an important component of the management of all children diagnosed with retinoblastoma. In this chapter, we will go over the history, genetics, and counseling for patients with retinoblastoma

Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset

U-Net-Based Models towards Optimal MR Brain Image Segmentation

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization

Coincident PAMM and AMN and Insights Into a Common Pathophysiology

Purpose: To analyze imaging characteristics and the clinical course of patients demonstrating coincident lesions of paracentral acute middle maculopathy (PAMM) and acute macular neuroretinopathy (AMN) in the same eye. Design: Retrospective, observational case series. Methods: Lesions from patients presenting with coincident PAMM and AMN in the same eye were evaluated with multimodal imaging including optical coherence tomography (OCT). The association with ocular and systemic findings was also investigated. Results: Fifteen subjects (17 eyes) were included in the study. The mean age was 44.4 ± 15.3 years and the follow-up period ranged from 1 to 32 weeks (mean, 11.9 ± 11.4 weeks). The mean visual acuity was 0.8 ± 0.6 logarithm of minimal angle of resolution (Snellen equivalent 20/126) at baseline and 0.3 ± 0.4 logarithm of minimal angle of resolution (Snellen equivalent 20/40) at the last follow-up. PAMM and AMN lesions occurred in the setting of Purtscher's retinopathy (4 eyes, 3 patients), retinal vein occlusion (7 eyes, 7 patients), central retinal artery occlusion (1 eye, 1 patient), and idiopathic retinal vasculitis (1 eye, 1 patient). In 4 eyes (3 patients), an association with other ocular disorders was not identified as evaluated with multimodal imaging. Of the total cohort, 11 eyes (64.7%) showed extension of the AMN hyperreflective bands in Henle's fiber layer with a Z-shaped morphology on OCT B-scan. Conclusions: The presence of coincident PAMM and AMN suggests a common pathophysiologic etiology. This may be the result of retinal vein impairment and hypoperfusion at the level of the deep retinal capillary plexus possibly leading to injury to the Müller glia or photoreceptors in Henle's fiber layer

Coincident PAMM and AMN and insights into a common pathophysiology

Purpose: To analyze imaging characteristics and the clinical course of patients demonstrating coincident lesions of paracentral acute middle maculopathy (PAMM) and acute macular neuroretinopathy (AMN) in the same eye. Design: Retrospective, observational case series. Methods: Lesions from patients presenting with coincident PAMM and AMN in the same eye were evaluated with multimodal imaging including optical coherence tomography (OCT). The association with ocular and systemic findings was also investigated. Results: Fifteen subjects (17 eyes) were included in the study. The mean age was 44.4 ± 15.3 years and the follow-up period ranged from 1 to 32 weeks (mean, 11.9 ± 11.4 weeks). The mean visual acuity was 0.8 ± 0.6 logarithm of minimal angle of resolution (Snellen equivalent 20/126) at baseline and 0.3 ± 0.4 logarithm of minimal angle of resolution (Snellen equivalent 20/40) at the last follow-up. PAMM and AMN lesions occurred in the setting of Purtscher's retinopathy (4 eyes, 3 patients), retinal vein occlusion (7 eyes, 7 patients), central retinal artery occlusion (1 eye, 1 patient), and idiopathic retinal vasculitis (1 eye, 1 patient). In 4 eyes (3 patients), an association with other ocular disorders was not identified as evaluated with multimodal imaging. Of the total cohort, 11 eyes (64.7%) showed extension of the AMN hyperreflective bands in Henle's fiber layer with a Z-shaped morphology on OCT B-scan. Conclusions: The presence of coincident PAMM and AMN suggests a common pathophysiologic etiology. This may be the result of retinal vein impairment and hypoperfusion at the level of the deep retinal capillary plexus possibly leading to injury to the Müller glia or photoreceptors in Henle's fiber layer. © 2021 Elsevier Inc

Transformer architecture-based transfer learning forpoliteness prediction in conversation

Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance.</p