Identifying Expert Reviews in the Crowd: Linking Curated and Noisy Domains

Over the past decade, vast number of online consumer reviews have made a significant presence on the Internet. These reviews play a vital role in consumer awareness about the products and deeply impact the consumer's decision-making process. On one hand, websites like Amazon, Yelp provide huge collections of crowd- sourced reviews, which are written by consumers themselves having experience in using that product. Many researchers argue about the credibility and bias of these reviews. These factors, coupled with the sheer plethora of reviews for each product, it can become tiring to form a perspective about the product. On other hand, websites like Wirecutter, Thesweetsetup provide hand-made highly curated detailed guides on products across various categories. Although these reviews are unbiased expert opinions, they require vigorous reporting, interviewing, and testing by various journalists, scientists, and researchers. Thus making them hard to scale. Our aim is to study the possible correlations between the crowd-sourced noisy domain reviews and the curated reviews. We take into account meta-features of re- views, context-based textual features of reviews and word-embedding based features of words from reviews. In addition to this, we identify “good reviews", defined as those noisy domain reviews that align with the curated ones, and use this to propose a general purpose, extremely streamlined recommender that can provide value to the general public without any personalized inputs. This research will contribute significantly towards identifying unbiased crowd-sourced reviews that align with curated reviews, across different categories of products, thereby linking the curated and noisy domains. Our research will also contribute significantly towards understanding the intricacies of good product reviews across different categories

Identifying Expert Reviews in the Crowd: Linking Curated and Noisy Domains

Over the past decade, vast number of online consumer reviews have made a significant presence on the Internet. These reviews play a vital role in consumer awareness about the products and deeply impact the consumer's decision-making process. On one hand, websites like Amazon, Yelp provide huge collections of crowd- sourced reviews, which are written by consumers themselves having experience in using that product. Many researchers argue about the credibility and bias of these reviews. These factors, coupled with the sheer plethora of reviews for each product, it can become tiring to form a perspective about the product. On other hand, websites like Wirecutter, Thesweetsetup provide hand-made highly curated detailed guides on products across various categories. Although these reviews are unbiased expert opinions, they require vigorous reporting, interviewing, and testing by various journalists, scientists, and researchers. Thus making them hard to scale. Our aim is to study the possible correlations between the crowd-sourced noisy domain reviews and the curated reviews. We take into account meta-features of re- views, context-based textual features of reviews and word-embedding based features of words from reviews. In addition to this, we identify “good reviews", defined as those noisy domain reviews that align with the curated ones, and use this to propose a general purpose, extremely streamlined recommender that can provide value to the general public without any personalized inputs. This research will contribute significantly towards identifying unbiased crowd-sourced reviews that align with curated reviews, across different categories of products, thereby linking the curated and noisy domains. Our research will also contribute significantly towards understanding the intricacies of good product reviews across different categories

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

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity Challenge’ where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the assumptions around bottleneck creation for the development of supervised learning methods.Open Acces