1,721,008 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Unsupervised domain adaptation for object detection and whole slide image classification
No full textDeep neural network (DNN) has been developed rapidly in years. While it shows promising results in various tasks of computer vision, DNN typically suffers from accuracy loss due to the domain shift from a source domain to a target domain. To mitigate the accuracy loss without the label from target domain, unsupervised domain adaptation (UDA) approaches are proposed.
Compare to most UDA studies that target image classification and pixel-level classification (image segmentation), UDA for object detection is a relatively new area. A popular processing pipeline is to apply adversarial training with domain discriminator. The domain discriminator aligns the feature distributions of the source and target domain.
Existing methods in UDA object detection extract features from image level and directly adapt the full features as in UDA for classification tasks. However, alignment on full image level features as a whole is not ideal for object detection task. The presence of varied backgrounds could interfere with the result of adaptation. To avoid alignment on a full feature, this thesis proposes a novel foreground-focused domain adaptation (FFDA) framework. This FFDA framework mines the loss of the domain discriminators so that the alignment could concentrate on the foreground during backpropagation.
FFDA collects target predictions and source image labels and uses them to generate mining masks that outline foreground regions. And then it applies the masks to image and instance level domain discriminators to allow backpropagation only on mined regions. In addition, by reinforcing this foreground-focused adaptation throughout multiple layers in the detector model, FFDA pushes the detector to gain a significant accuracy boost on target domain prediction. Compared with previous methods, FFDA method reaches the new state-of-the-art accuracy on adaptation from Cityscape to Foggy Cityscape dataset. The FFDA also demonstrates competitive results on other datasets that include various scenarios for autonomous driving applications.
In addition to object detection problem, this thesis also discusses the application of UDA for whole slide image (WSI) classification. Image classification for WSI is a challenging task compared to general image classification because of its high resolution and scattered key information. Previous work provided a novel deep Fisher vector coding pipeline for WSI classification. However, this pipeline suffers from the same accuracy drop phenomenon when deployed to another set of WSI from a different institution to perform the same task. This poses a limitation of the practical usage of the pipeline especially when the diagnoses of WSIs are hard to obtain.
On the other hand, previous works that apply UDA to medical imaging typically focused on adapting on small microscopy image samples or image patches extracted from WSI. UDA for the application of classifying the entire WSI has not yet been discussed due to the limited number of pipelines and datasets that support WSI classification.
This thesis aims at providing a UDA solution to enhance the robustness of the previous pipeline by mitigating the accuracy drop caused by different WSI datasets. This solution inserts the domain classifiers into the previous pipeline in different stages to align the features during training. The solution is evaluated by calculating confusion matrices before and after the adaptation. The results demonstrate that by placing domain classifiers in different stages the pipeline shows an accuracy boost on target WSI data
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Mixed Low-bit Quantization for Model Compression with Layer Importance and Gradient Estimations
Full text linkDeep neural networks (DNNs) have been widely used in the modern world in recent years. However, due to the substantial memory consumption and high computational power use of DNNs, deploying them on devices with limited resources is challenging. Model compression methods can provide us with a remedy here. Among those techniques, neural network quantization has achieved a high compression rate using a low bitwidth representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed precision quantization. In this thesis, We propose an accuracy-aware criterion to quantify the layer’s importance rank. Our method applies imprinting per layer, which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize those with less accuracy. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation
to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration. We have also pointed out the problem of
the Straight-Through Estimator (STE), which is commonly used for gradients estimation in the quantization field. We’ve discussed some ways to address the problem of using STE
A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction
Full text linkHistological examination and derived ancillary testing remain the gold standard for breast cancer diagnosis, prognosis assessment and treatment guidance. Currently, a commercial molecular signature test OncotypeDX®, based on RNA quantitation and providing a recurrence score (RS) ranging from 0 to 100, is routinely utilized for luminal breast cancers (the largest sub-type group of breast cancers) to predict the probabilities of response to chemotherapy and disease recurrence. We attempt to predict RS using digital pathology and Weakly Supervised (WS) attention-based models. In tissue samples, the malignant component is haphazardly admixed with the non-malignant component in variable proportions. This represents a challenge for WS attention-based models to identify high-valued diagnostic/prognostic areas within whole slide images (WSIs). To address this, we propose an interactive, supervised approach with a human in the middle by creating a user-friendly Graphical User Interface (GUI) that allows an expert pathologist to annotate heatmaps generated by any WS attention-based model. We aim to enhance the model’s learning capabilities and performance by incorporating the feedback from the GUI as expected scores in the successive training process. We train WS attention-based models like CLAM (Clustering-constrained Attention Multiple Instance Learning) and TransMIL (Transformer based Correlated Multiple Instance Learning) on our in-house dataset before and after the expert feedback. We observe an improvement in RS prediction after retraining both models with the pathologist’s annotation- a 5% rise in validation-test AUC and 4% in validation-test accuracy for CLAM and a 4.5% increase in validation-test AUC and 3% in validation-test accuracy for TransMIL. We analyze the generated heatmaps and observe how additional supervision from a domain expert enhances the learning capacity of the models. We notice an improvement in cosine similarity between the pathologist’s GUI-based attention scores and trained models’ attention maps after feedback - 5% and 10% increase for CLAM and TransMIL, respectively. Our adaptive, interactive system harmonizes attention scores with expert intuition and instills higher confidence in the system’s predictions. This study establishes a potent synergy between AI and expert collaboration, addressing the constraints of WS by enhancing the discrimination of diagnostic features and making an effort to generate predictions according to clinical diagnostic norms
End-to-End Learning of Dynamic Programming and Convolutional Neural Networks using Differentiable Bypass
No full textDifferentiable Programming is the paradigm where different functions or modules are combined into a unified pipeline with the purpose of applying end-to-end learning or optimization. A natural impediment is the non-differentiability characteristic of many modules. This thesis proposes a new way to overcome this obstacle by using a concept called Differentiable Bypass (DiffBypass). DiffBypass exploits the Universal Function Approximation property of neural networks to mimic the output of non-differentiable functions or modules in the pipeline, rerouting the gradient path to bypass these components entirely.Further, as a significant application, we demonstrate the use of DiffBypass to combine Convolutional Neural Networks (CNN) and Dynamic Programming (DP) in end-to-end learning for segmenting left ventricle from short axis view of heart MRI. Our experiments show that end-to-end combination of CNN and DP requires fewer labeled images to achieve a significantly better segmentation accuracy than using only CNN by allowing the incorporation of strong prior knowledge into the pipeline to cope with lack of training data. Comparison between DiffBypass and Evolution Strategy (ES), another method that can be used to train non-differentiable modules, shows that DiffBypass is more robust and has better performance for high-dimension problems.Finally, as a technical contribution, we provide a set of recommendations for training non-differentiable modules using DiffBypass. Furthermore, we also provide a code base for reproducibility. We think DiffBypass has the potential to become a blueprint to expand differentiable programming to include non-differentiable modules
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