Deep neural network or dermatologist?

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification

EczemaNet: automating detection and severity assessment of atopic dermatitis

Atopic dermatitis (AD), also known as eczema, is one of themost common chronic skin diseases. AD severity is primarily evaluatedbased on visual inspections by clinicians, but is subjective and has largeinter- and intra-observer variability in many clinical study settings. Toaid the standardisation and automating the evaluation of AD severity,this paper introduces a CNN computer vision pipeline, EczemaNet, thatfirst detects areas of AD from photographs and then makes probabilisticpredictions on the severity of the disease. EczemaNet combines trans-fer and multitask learning, ordinal classification, and ensembling overcrops to make its final predictions. We test EczemaNet using a set of im-ages acquired in a published clinical trial, and demonstrate low RMSEwith well-calibrated prediction intervals. We show the effectiveness of us-ing CNNs for non-neoplastic dermatological diseases with a medium-sizedataset, and their potential for more efficiently and objectively evaluatingAD severity, which has greater clinical relevance than mere classification

Deep Learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review

Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10-15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important immuno-oncological biomarker in CRC. Gold standard tests for detection of MSI/dMMR in CRC are based on wet laboratory tests such as immunohistochemistry (IHC) or DNA extraction with subsequent polymerase chain reaction (PCR). However, since 2019, advances in Deep Learning (DL), an Artificial Intelligence (AI) technology, have enabled the prediction of MSI/dMMR directly from digitized routine haematoxylin and eosin (H&E) histopathology slides with high accuracy. In addition to the initial proof-of-concept publication in 2019, twelve subsequent studies have refined, improved, and further validated this approach. At this moment, MSI/dMMR prediction using Deep Learning has become a widely used benchmark task for academic studies in the field of computational pathology. Beyond academic use, this assay has attracted commercial interest from companies with the possibility of approval as a diagnostic device in the near future. In this review, we summarize and quantitatively compare the existing evidence on Deep-Learning-based detection of MSI/dMMR in CRC and discuss the need for further improvement and potential for integration into routine pathological workflows. Ultimately, this DL-based method could facilitate the identification of patients eligible for treatment with immune checkpoint inhibitors by pre-screening or replacement of current methods

Higher occurrence of nausea and vomiting after total hip arthroplasty using general versus spinal anesthesia: an observational study.

BACKGROUND: Under the assumption that postoperative nausea and vomiting (PONV) may occur after total hip arthroplasty (THA) regardless of the anesthetic technique used, it is not clear whether general (GA) or spinal (SA) anesthesia has higher causal effect on this occurrence. Conflicting results have been reported. METHODS: In this observational study, we selected all elective THA interventions performed in adults between 1999 and 2008 in a Swiss orthopedic clinic under general or spinal anesthesia. To assess the effect of anesthesia type on the occurrence of PONV, we used the propensity score and matching methods, which allowed us to emulate the design and results of an RCT. RESULTS: Among 3922 procedures, 1984 (51 %) patients underwent GA, of which 4.1 % experienced PONV, and 1938 underwent SA, of which 3.5 % experienced PONV. We found that the average treatment effect on the treated, i.e. the effect of anesthesia type for a sample of individuals that actually received spinal anesthesia compared to individuals who received GA, was ATET = 2.00 % [95 % CI, 0.78-3.19 %], which translated into an OR = 1.97 [95 % CI 1.35; 2.87]. CONCLUSION: This suggests that the type of anesthesia is not neutral regarding PONV, general anesthesia being more strongly associated with PONV than spinal anesthesia in orthopedic surgery

Instrumentation for fluorescence lifetime measurement using photon counting

We describe the evolution of HORIBA Jobin Yvon IBH Ltd, and its time-correlated single-photon counting (TCSPC) products, from university research beginnings through to its present place as a market leader in fluorescence lifetime spectroscopy. The company philosophy is to ensure leading-edge research capabilities continue to be incorporated into instruments in order to meet the needs of the diverse range of customer applications, which span a multitude of scientific and engineering disciplines. We illustrate some of the range of activities of a scientific instrument company in meeting this goal and highlight by way of an exemplar the performance of the versatile DeltaFlex instrument in measuring fluorescence lifetimes. This includes resolving fluorescence lifetimes down to 5 ps, as frequently observed in energy transfer, nanoparticle metrology with sub-nanometre resolution and measuring a fluorescence lifetime in as little as 60 μs for the study of transient species and kinetics

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

Land Cover and Rainfall Interact to Shape Waterbird Community Composition

Human land cover can degrade estuaries directly through habitat loss and fragmentation or indirectly through nutrient inputs that reduce water quality. Strong precipitation events are occurring more frequently, causing greater hydrological connectivity between watersheds and estuaries. Nutrient enrichment and dissolved oxygen depletion that occur following these events are known to limit populations of benthic macroinvertebrates and commercially harvested species, but the consequences for top consumers such as birds remain largely unknown. We used non-metric multidimensional scaling (MDS) and structural equation modeling (SEM) to understand how land cover and annual variation in rainfall interact to shape waterbird community composition in Chesapeake Bay, USA. The MDS ordination indicated that urban subestuaries shifted from a mixed generalist-specialist community in 2002, a year of severe drought, to generalist-dominated community in 2003, of year of high rainfall. The SEM revealed that this change was concurrent with a sixfold increase in nitrate-N concentration in subestuaries. In the drought year of 2002, waterbird community composition depended only on the direct effect of urban development in watersheds. In the wet year of 2003, community composition depended both on this direct effect and on indirect effects associated with high nitrate-N inputs to northern parts of the Bay, particularly in urban subestuaries. Our findings suggest that increased runoff during periods of high rainfall can depress water quality enough to alter the composition of estuarine waterbird communities, and that this effect is compounded in subestuaries dominated by urban development. Estuarine restoration programs often chart progress by monitoring stressors and indicators, but rarely assess multivariate relationships among them. Estuarine management planning could be improved by tracking the structure of relationships among land cover, water quality, and waterbirds. Unraveling these complex relationships may help managers identify and mitigate ecological thresholds that occur with increasing human land cover

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer

Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008

SCOPUS: ar.jinfo:eu-repo/semantics/publishe