We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models.
As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research.
In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search.
It is well established in neuroscience that color vision plays an essential part in the human visual perception system.
Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies.
This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers.
In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions.
Truckload brokerages, a $100 billion/year industry in the U. S., plays the critical role of matching shippers with carriers, often to move loads several days into the future.
The Matlab-based simulator allows the comparison of a number of learning policies (represented as a series of . m modules) in the context of a wide range of problems (each represented in its own . m module) which makes it easy to add new algorithms and new test problems.
We also show that the knowledge gradient policy is asymptotically optimal in an offline setting.
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals.
Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance.
We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives.
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules.