Automated reporting of cervical biopsies using artificial intelligence.

When detected at an early stage, the 5-year survival rate for people with invasive cervical cancer is 92%. Being aware of signs and symptoms of cervical cancer and early detection greatly improve the chances of successful treatment. We have developed an Artificial Intelligence (AI) algorithm, trained and evaluated on cervical biopsies for automated reporting of digital diagnostics.

Correction: Guo et al. A Siamese Transformer Network for Zero-Shot Ancient Coin Classification. 2023, , 107.

Jochen Büttner was not included as an author in the original publication [...

Magnifying Networks for Histopathological Images with Billions of Pixels.

Amongst the other benefits conferred by the shift from traditional to digital pathology is the potential to use machine learning for diagnosis, prognosis, and personalization. A major challenge in the realization of this potential emerges from the extremely large size of digitized images, which are often in excess of 100,000 × 100,000 pixels. In this paper, we tackle this challenge head-on by diverging from the existing approaches in the literature-which rely on the splitting of the original images into small patches-and introducing magnifying networks (MagNets).

Disease: An ill-founded concept at odds with the principle of patient-centred medicine.

Background: Despite the at least decades long record of philosophical recognition and interest, the intricacy of the deceptively familiar appearing concepts of 'disease', 'disorder', 'disability', and so forth, has only recently begun showing itself with clarity in the popular discourse wherein its newly emerging prominence stems from the liberties and restrictions contingent upon it. Whether a person is deemed to be afflicted by a disease or a disorder governs their ability to access health care, be it free at the point of use or provided by an insurer; it also influences the treatment of individuals by the judicial system and employers; it even affects one's own perception of self.

Aims: All existing philosophical definitions of disease struggle with coherency, causing much confusion and strife, and leading to inconsistencies in real-world practice.

Localization and phenotyping of tuberculosis bacteria using a combination of deep learning and SVMs.

Successful treatment of pulmonary tuberculosis (TB) depends on early diagnosis and careful monitoring of treatment response. Identification of acid-fast bacilli by fluorescence microscopy of sputum smears is a common tool for both tasks. Microscopy-based analysis of the intracellular lipid content and dimensions of individual Mycobacterium tuberculosis (Mtb) cells also describe phenotypic changes which may improve our biological understanding of antibiotic therapy for TB.

A Siamese Transformer Network for Zero-Shot Ancient Coin Classification.

Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods.

Caveat Medicus: It's Time to Re-Think Stratification, You May Not Be Helping.

Background: The focus of the present Letter is on the large and seemingly fertile body of work captured under the umbrella of 'patient stratification'.

Objectives: I identify and explain a fundamental methodological flaw underlying the manner in which the development of an increasingly large number of new stratification strategies is approached.

Design: I show an inherent conflict between the assumptions made, and the very purpose of stratification and its application in practice.

Resolving the ethical quagmire of the persistent vegetative state.

Background: A patient is diagnosed with the persistent vegetative state (PVS) when they show no evidence of the awareness of the self or the environment for an extended period of time. The chance of recovery of any mental function or the ability to interact in a meaningful way is low. Though rare, the condition, considering its nature as a state outwith the realm of the conscious, coupled with the trauma experienced by the patient's kin as well as health care staff confronted with painful decisions regarding the patient's care, has attracted a considerable amount of discussion within the bioethics community.

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence.

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope.

Reproducibility of deep learning in digital pathology whole slide image analysis.

For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments.

Lymphocyte Classification from Hoechst Stained Slides with Deep Learning.

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, these techniques are expensive.

Use of High-Plex Data Reveals Novel Insights into the Tumour Microenvironment of Clear Cell Renal Cell Carcinoma.

Although immune checkpoint inhibitors (ICIs) have significantly improved the oncological outcomes, about one-third of patients affected by clear cell renal cell carcinoma (ccRCC) still experience recurrence. Current prognostic algorithms, such as the Leibovich score (LS), rely on morphological features manually assessed by pathologists and are therefore subject to bias. Moreover, these tools do not consider the heterogeneous molecular milieu present in the Tumour Microenvironment (TME), which may have prognostic value.

A systemic challenge in dietetics: Methodological inadequacies, erroneous claims, and misleadinginterpretations, and transparency of post-publication scrutiny.

Obesity is sweeping across the developed world. Yet, the public remains largely confused when it comes to the nature of dietary habits which would serve to counteract this trend. I highlight the responsibility that the scientific community bears when it comes to the confusion, and explain the kind of actions that are needed if the public trust in science is to be maintained.

Generative Image Inpainting for Retinal Images using Generative Adversarial Networks.

The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed. However, a common drawback of these devices is a limited field of view.

How Good is the Science That Informs Government Policy? A Lesson From the U.K.'s Response to 2020 CoV-2 Outbreak.

In an era when public faith in politicians is dwindling, yet trust in scientists remains relatively high, governments are increasingly emphasizing the role of science based policy-making in response to challenges such as climate change and global pandemics. In this paper we question the quality of some scientific advice given to governments and the robustness and transparency of the entire framework which envelopes such advice, all of which raise serious ethical concerns. In particular we focus on the so-called Imperial Model which heavily influenced the government of the United Kingdom in devising its response to the COVID-19 crisis.

Tracking of Deformable Objects Using Dynamically and Robustly Updating Pictorial Structures.

The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent.

Determining Chess Game State from an Image.

Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately. However, it is important for helping amateur chess players improve their games by facilitating automatic computer analysis without the overhead of manually entering the pieces. Current approaches are limited by the lack of large datasets and are not designed to adapt to unseen chess sets.

A Comparison of Methods for Studying the Tumor Microenvironment's Spatial Heterogeneity in Digital Pathology Specimens.

Background: The tumor microenvironment is highly heterogeneous, and it is understood to affect tumor progression and patient outcome. A number of studies have reported the prognostic significance of tumor-infiltrating lymphocytes and tumor budding in colorectal cancer (CRC). However, the significance of the intratumoral heterogeneity present in the spatial distribution of these features within the tumor immune microenvironment (TIME) has not been previously reported.

Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.

The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1.

Quantification of Advanced Dementia Patients' Engagement in Therapeutic Sessions: An Automatic Video Based Approach using Computer Vision and Machine Learning.

Most individuals with advanced dementia lose the ability to communicate with the outside world through speech. This limits their ability to participate in social activities crucial to their well-being and quality of life. However, there is mounting evidence that individuals with advanced dementia can still communicate non-verbally and benefit greatly from these interactions.

Corrigendum: Deep Learning for Whole Slide Image Analysis: An Overview.

[This corrects the article on p. 264 in vol. 6, PMID: 31824952.

Big Data Driven Detection of Trees in Suburban Scenes Using Visual Spectrum Eye Level Photography.

The aim of the work described in this paper is to detect trees in eye level view images. Unlike previous work that universally considers highly constrained environments, such as natural parks and wooded areas, or simple scenes with little clutter and clear tree separation, our focus is on much more challenging suburban scenes, which are rich in clutter and highly variable in type and appearance (houses, falls, shrubs, cars, bicycles, pedestrians, hydrants, lamp posts, etc.).

Cold beverage-induced vasovagal syncope in a healthy young adult man: a case report.

Background: Swallowing-induced syncope is rare and there are few case reports of it in the existing medical literature. Even rarer are instances involving young and healthy individuals, with no existing pre-conditions or apparent risk factors. Hence the value of such case reports in understanding the phenomenon better and potentially inferring patterns of practical interest is significant; here we describe an unusual case of a swallowing-induced syncope in a young, healthy, and active white man.

Deep Learning for Whole Slide Image Analysis: An Overview.

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts.

A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis.

Accurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage II colorectal cancer patients using the current TNM guidelines.