Deep weakly-supervised learning methods for classification and localization in histology images: a survey

Using state-of-the-art deep learning models for cancer diagnosis presents several challenges related to the nature and availability of labeled histology images. In particular, cancer grading and localization in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. In this survey, deep weakly-supervised learning (WSL) models are investigated to identify and locate diseases in histology images, without the need for pixel-level annotations. Given training data with global image-level labels, these models allow to simultaneously classify histology images and yield pixel-wise localization scores, thereby identifying the corresponding regions of interest (ROI). Since relevant WSL models have mainly been investigated within the computer vision community, and validated on natural scene images, we assess the extent to which they apply to histology images which have challenging properties, e.g. very large size, similarity between foreground/background, highly unstructured regions, stain heterogeneity, and noisy/ambiguous labels. The most relevant models for deep WSL are compared experimentally in terms of accuracy (classification and pixel-wise localization) on several public benchmark histology datasets for breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS. Furthermore, for large-scale evaluation of WSL models on histology images, we propose a protocol to construct WSL datasets from Whole Slide Imaging. Results indicate that several deep learning models can provide a high level of classification accuracy, although accurate pixel-wise localization of cancer regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure

CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos

Weakly supervised video object localization (WSVOL) methods often rely on visual and motion cues only, making them susceptible to inaccurate localization. Recently, discriminative models have been explored using a temporal class activation mapping (CAM) method. Although their results are promising, objects are assumed to have limited movement from frame to frame, leading to degradation in performance for relatively long-term dependencies. In this paper, a novel CoLo-CAM method for WSVOL is proposed that leverages spatiotemporal information in activation maps during training without making assumptions about object position. Given a sequence of frames, explicit joint learning of localization is produced based on color cues across these maps, by assuming that an object has similar color across adjacent frames. CAM activations are constrained to respond similarly over pixels with similar colors, achieving co-localization. This joint learning creates direct communication among pixels across all image locations and over all frames, allowing for transfer, aggregation, and correction of learned localization, leading to better localization performance. This is achieved by minimizing the color term of a conditional random field (CRF) loss over a sequence of frames/CAMs. Empirical experiments on two challenging datasets with unconstrained videos, YouTube-Objects, show the merits of our method, and its robustness to long-term dependencies, leading to new state-of-the-art performance for WSVOL.Comment: 16 pages, 8 figure

A qualitative, grounded theory exploration of the determinants of self-care behavior among Indian patients with a lived experience of chronic heart failure

Prior reports have documented extremely poor adherence to evidence-based medications among South Asian patients with established chronic cardiovascular diseases. Treatment adherence is now considered a part of the ‘self-care’ process, the determinants of which have not been adequately explored or explained among South Asian patients with chronic heart failure (CHF). Our objective was to qualitatively ascertain the determinants of the selfcare process among Indian patients with a lived experience of heart failure

Broadly neutralizing antibody responses in the longitudinal primary HIV-1 infection Short Pulse Anti-Retroviral Therapy at Seroconversion cohort

OBJECTIVE: Development of immunogens that elicit an anti-HIV-1 broadly neutralizing antibody (bnAb) response will be a key step in the development of an effective HIV-1 vaccine. Although HIV-1 bnAb epitopes have been identified and mechanisms of action studied, current HIV-1 envelope-based immunogens do not elicit HIV-1 bnAbs in humans or animal models. A better understanding of how HIV-1 bnAbs arise during infection and the clinical factors associated with bnAb development may be critical for HIV-1 immunogen design efforts. DESIGN AND METHODS: Longitudinal plasma samples from the treatment-naive control arm of the Short Pulse Anti-Retroviral Therapy at Seroconversion (SPARTAC) primary HIV-1 infection cohort were used in an HIV-1 pseudotype neutralization assay to measure the neutralization breadth, potency and specificity of bnAb responses over time. RESULTS: In the SPARTAC cohort, development of plasma neutralization breadth and potency correlates with duration of HIV infection and high viral loads, and typically takes 3-4 years to arise. bnAb activity was mostly directed to one or two bnAb epitopes per donor and more than 60% of donors with the highest plasma neutralization having bnAbs targeted towards glycan-dependent epitopes. CONCLUSION: This study highlights the SPARTAC cohort as an important resource for more in-depth analysis of bnAb developmental pathways

Molecular Evolution of Broadly Neutralizing Llama Antibodies to the CD4-Binding Site of HIV-1

To date, no immunization of humans or animals has elicited broadly neutralizing sera able to prevent HIV-1 transmission; however, elicitation of broad and potent heavy chain only antibodies (HCAb) has previously been reported in llamas. In this study, the anti-HIV immune responses in immunized llamas were studied via deep sequencing analysis using broadly neutralizing monoclonal HCAbs as a guides. Distinct neutralizing antibody lineages were identified in each animal, including two defined by novel antibodies (as variable regions called VHH) identified by robotic screening of over 6000 clones. The combined application of five VHH against viruses from clades A, B, C and CRF_AG resulted in neutralization as potent as any of the VHH individually and a predicted 100% coverage with a median IC50 of 0.17 µg/ml for the panel of 60 viruses tested. Molecular analysis of the VHH repertoires of two sets of immunized animals showed that each neutralizing lineage was only observed following immunization, demonstrating that they were elicited de novo. Our results show that immunization can induce potent and broadly neutralizing antibodies in llamas with features similar to human antibodies and provide a framework to analyze the effectiveness of immunization protocols

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols

A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

BACKGROUND:Genetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models. METHODS:We compared GP against the commonly used Cox regression technique in terms of the development and performance of a cardiovascular risk score using data from the SMART study, a prospective cohort study of patients with symptomatic cardiovascular disease. The composite endpoint was cardiovascular death, non-fatal stroke, and myocardial infarction. A total of 3,873 patients aged 19-82 years were enrolled in the study 1996-2006. The cohort was split 70:30 into derivation and validation sets. The derivation set was used for development of both GP and Cox regression models. These models were then used to predict the discrete hazards at t = 1, 3, and 5 years. The predictive ability of both models was evaluated in terms of their risk discrimination and calibration using the validation set. RESULTS:The discrimination of both models was comparable. At time points t = 1, 3, and 5 years the C-index was 0.59, 0.69, 0.64 and 0.66, 0.70, 0.70 for the GP and Cox regression models respectively. At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was also comparable, but with the Cox model being better calibrated to the validation data. CONCLUSION:Using empirical data, we demonstrated that a prediction model developed automatically by GP has predictive ability comparable to that of manually tuned Cox regression. The GP model was more complex, but it was developed in a fully automated way and comprised fewer covariates. Furthermore, it did not require the expertise normally needed for its derivation, thereby alleviating the knowledge elicitation bottleneck. Overall, GP demonstrated considerable potential as a method for the automated development of clinical prediction models for diagnostic and prognostic purposes