A Deeper Analysis of Volumetric Relightable Faces.

Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handling both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with individual latent spaces for identity and illumination. The prior model is learned in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis, even when applied to unseen subjects under uncontrolled illumination. This work is an extension of Rao et al. (VoRF: Volumetric Relightable Faces 2022). We provide extensive evaluation and ablative studies of our model and also provide an application, where any face can be relighted using textual input.

Peritoneal mesometrial resection with lymphadenectomy following prior hysterectomy in intermediate/high-risk endometrial cancer: feasibility and safety.

OBJECTIVE: Peritoneal mesometrial resection (PMMR) plus targeted compartmental lymphadenectomy (TCL) aims at removal of the locoregional cancer field in endometrial cancer (EC). Optimal locoregional control without adjuvant radiotherapy should be achieved concomitantly sparing systematic lymphadenectomy (LNE) for most of the patients. However, intermediate/high-risk EC is often definitely diagnosed postoperatively in simple hysterectomy specimen. Our aim was to evaluate feasibility and safety of a completing PMMR + TCL in patients following prior hysterectomy. METHODS: We evaluated data from 32 patients with intermediate/high-risk EC treated with PMMR + TCL or systematic pelvic and periaortic LNE following prior hysterectomy. Perioperative data on disease characteristics and morbidity were collected and patients were contacted for follow-up to determine the recurrence and survival status. RESULTS: We report data from 32 patients with a mean follow-up of 31.7 months. The recurrence rate was 12.5% (4/32) without any isolated locoregional recurrences. Only 21.9% of patients received adjuvant radiotherapy. Rates of intra- and postoperative complications were 6.3% and 18.8%, respectively. CONCLUSION: Our data suggest that robotic PMMR can be performed following prior hysterectomy when previously unknown risk factors arise, albeit with a moderate increase in morbidity. Moreover, despite a relevant reduction of adjuvant radiotherapy, follow-up data suggest an excellent locoregional control even without adjuvant radiotherapy.

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions.

Generative adversarial networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images, whereas the discriminator determines their authenticity by comparing them with real examples. Through iterative training, the generator allows the creation of images that are indistinguishable from real ones, leading to high-quality image generation. Considering their success in computer vision, GANs hold great potential for medical diagnostic applications. In the medical field, GANs can generate images of rare diseases, aid in learning, and be used as visualization tools. GANs can leverage unlabeled medical images, which are large in size, numerous in quantity, and challenging to annotate manually. GANs have demonstrated remarkable capabilities in image synthesis and have the potential to significantly impact digital histopathology. This review article focuses on the emerging use of GANs in digital histopathology, examining their applications and potential challenges. Histopathology plays a crucial role in disease diagnosis, and GANs can contribute by generating realistic microscopic images. However, ethical considerations arise because of the reliance on synthetic or pseudogenerated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, digital histopathology has seen an emerging use of GANs for image enhancement, such as color (stain) normalization, virtual staining, and ink/marker removal. GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.

Egyptian Society of Liver Cancer Recommendation Guidelines for the Management of Hepatocellular Carcinoma.

Globally, hepatocellular carcinoma (HCC) is the fourth most common cause of death from cancer. The prevalence of this pathology, which has been on the rise in the last 30 years, has been predicted to continue increasing. HCC is the most common cause of cancer-related morbidity and mortality in Egypt and is also the most common cancer in males. Chronic liver diseases, including chronic hepatitis C, which is a primary health concern in Egypt, are considered major risk factors for HCC. However, HCC surveillance is recommended for patients with chronic hepatitis B virus (HBV) and liver cirrhosis; those above 40 with HBV but without cirrhosis; individuals with hepatitis D co-infection or a family history of HCC; and Nonalcoholic fatty liver disease (NAFLD) patients exhibiting significant fibrosis or cirrhosis. Several international guidelines aid physicians in the management of HCC. However, the availability and cost of diagnostic modalities and treatment options vary from one country to another. Therefore, the current guidelines aim to standardize the management of HCC in Egypt. The recommendations presented in this report represent the current management strategy at HCC treatment centers in Egypt. Recommendations were developed by an expert panel consisting of hepatologists, oncologists, gastroenterologists, surgeons, pathologists, and radiologists working under the umbrella of the Egyptian Society of Liver Cancer. The recommendations, which are based on the currently available local diagnostic aids and treatments in the country, include recommendations for future prospects.

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.

BACKGROUND: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage. DISCUSSION: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed. CONCLUSION: Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.

Efficacy of loco-regional treatment for hepatocellular carcinoma prior to living donor liver transplantation: a report from a single center in Egypt.

BACKGROUND AND AIM: The number of loco-regional therapies (LRTs) for hepatocellular carcinoma (HCC) has increased dramatically during the past decade, bridging or downstaging patients on the waiting list for liver transplantation. This study aimed to analyze the outcomes of LRTs prior to living donor liver transplantation in patients with HCC. METHODS: Sixty-two HCC patients received living donor liver transplantation at Ain Shams Center for Organ Transplantation over a 2-year period. Data from 29 HCC patients were analyzed. Twenty patients (68.97%) met the Milan Criteria and 4 patients (13.8%) exceeded the Milan Criteria, but met the University of California, San Francisco Criteria. Five patients (17.2%) exceeded the University of California, San Francisco Criteria. All patients underwent preoperative LRTs. The protocol of bridging/downstaging, methods, duration of follow-up, the number of patients who were successfully downstaged before liver transplantation (LT), and their outcomes after LT were recorded. RESULTS: There was a decrease in the mean overall size of focal lesions (from mean 5.46 to 4.11 cm) in the last abdominal computed tomography (CT) scan after LRT (p=0.0018). Discrepancies between the radiological findings and histopathology were as follows: in 16 patients (55.17%) the CT findings were consistent with the histopathological examination of the explanted liver. Underestimated tumor stage was documented in 10 patients (34.48%), and was overestimated by CT scan findings in 3 patients (10.34%). The 1-year survival rate was 93%. No patient had HCC recurrence after median follow-up of 21 months (range 1-46 months). CONCLUSION: These results encouraged tumor bridging/downstaging as a potential treatment option among carefully selected patients with HCC beyond conventional criteria for LT. Further studies on a large number of patients are necessary.