Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks

Interleaved text/image Deep Mining on a large-scale radiology database

Despite tremendous progress in computer vision, effec-tive learning on very large-scale (> 100K patients) medi-cal image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital’s picture archiv-ing and communication system. Instead of using full 3D medical volumes, we focus on a collection of representa-tive ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector la-bels. Our system interleaves between unsupervised learn-ing (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demon-strated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their cat-egorization, embedded vector labels and sentence descrip-tions can be harnessed to alleviate the deep learning “data-hungry ” obstacle in the medical domain

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals

Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover meaningful counterfactuals and efficiently label them, with minimal human labeling cost. Most existing methods either completely rely on human-annotated labels, an expensive process which limits the scale of counterfactual data, or implicitly assume label invariance, which may mislead the model with incorrect labels. In this paper, we present a novel framework that utilizes counterfactual generative models to generate a large number of diverse counterfactuals by actively sampling from regions of uncertainty, and then automatically label them with a learned pairwise classifier. Our key insight is that we can more correctly label the generated counterfactuals by training a pairwise classifier that interpolates the relationship between the original example and the counterfactual. We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks

A strategy to gradual implementation of data interoperability

Data interoperability is a major concern on e-government, both from the point of view of service offering and from the point of view of public administration efficiency. This paper purposes an incremental, pragmatic approach to data interoperability. It is argued that integration with minor required initial efforts from institutions is feasible, may provide useful solutions and is a solid ground basis for subsequent evolution. This paper presents general guidelines and model solutions to support this approach. Also, presents a demo implementation that proves feasibility of the purposed models and delivers useful solutions on a specific business e-government scenario. Although still limited in range and demonstrated on a quite specific business environment, it is expected that the analysis and the proposed strategies, solutions and models be of interest on a larger spectrum of data interoperability problems.info:eu-repo/semantics/acceptedVersio

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain

Enhancing the Performance of the MtCNN for the Classification of Cancer Pathology Reports: From Data Annotation to Model Deployment

Information contained in electronic health records (EHR) combined with the latest advances in machine learning (ML) have the potential to revolutionize the medical sciences. In particular, information contained in cancer pathology reports is essential to investigate cancer trends across the country. Unfortunately, large parts of information in EHRs are stored in the form of unstructured, free-text which limit their usability and research potential. To overcome this accessibility barrier, cancer registries depend on expert personnel who read, interpret, and extract relevant information. Naturally, as the number of stored pathology reports increases every day, depending on human experts presents scalability challenges. Recently, researchers have attempted to automate the information extraction process from cancer pathology reports using ML techniques commonly found in natural language processing (NLP). However, clinical text is inherently different than other common forms of text, and state-of-the-art NLP approaches often exhibit mediocre performance. In this study, we narrow the literature gap by investigating methods to tackle overfitting and improve the performance of ML models for the classification of cancer pathology reports so that we can reduce the dependency on human expert annotators. We (1) show that using active learning can mitigate extreme class imbalance by increasing the representation of documents belonging to rare cancer types, (2) investigated the feasibility of ensemble learning and a mixture-of-expert variant to boost minority class performance, and (3) demonstrated that ensemble model distillation provides a strategy for quantifying the uncertainty inherent in labeled data, offering an effective low-resource solution that can be easily deployed by cancer registries

This is not a real image:Generative artificial intelligence to enhance radiology education

Radiologists fulfill a critical role in our healthcare system, but their workload has increased substantially over time. Although algorithmic tools have been proposed to support the diagnostic process, the workload is not efficiently decreased in this manner. However, another possibility is to decrease workload in a different area. The main topic of this thesis is concerned with investigating how simulation training can be realized to aid in the image interpretation skills training of the radiology resident. To realize simulated training it is necessary to know (1) how we can create realistic artificial medical images, subsequently (2) How we can control their variety and (3) how we can adjust their difficulty.Firstly, it is shown that artificial medical images can blend in with original ones. For this purpose a GAN model is used to create 2-dimensional artificial medical images. The created artificial images are assessed both quantitatively and qualitatively in terms of their realism. Secondly, to better control the variety of the artificial medical images a diffusion model is used to guide both coarse- and fine-features. The results show that the model was able to adjust fine-feature characteristics of the pathology type according to the feedback of the independent classifier. Thirdly, a method is presented to describe the detection difficulty of an (artificial) medical image using quantitative pathology and image characteristics. Results show that it is possible to describe almost two thirds of the variation in difficulty using these quantitative characteristics and as such describe images as having lower or higher detection difficulty. Finally, the responsible implementation of the medical image simulator to assist in image interpretation skills is investigated. Combining the results of this thesis resulted in a prototype of a 'medical image simulator'. This simulator can take over part of the workload of the supervising radiologists, by providing a means for independent repetitive practice for the resident. The realistic artificial medical images can be varied in terms of their content and their difficulty. This can enable a personalized experience that can enhance training of image interpretation skills and make it more efficient