For as long as she can remember, Allen School professor Su-In Lee wanted to be a scientist and professor when she grew up. Her father would sit her down at their home in Korea with a pencil and paper...

Nature Biomedical Engineering - Leveraging the expertise of physicians to identify medically meaningful features in ‘counterfactual’ images produced via generative machine learning...

Nature Communications - Series of machine learning models, relevant for tasks in biology, medicine, and finance, usually involve complex feature attribution techniques. The authors introduce a...

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprece...

Communications Medicine - Qiu et al. present a new approach, IMPACT, that uses explainable artificial intelligence to analyze all-cause mortality. IMPACT provides insights into the individualized...

Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We examine the literature...

Fluorescence in situ hybridization (FISH) is a widely used method for visualizing gene expression in cells and tissues. A key challenge is determining a small panel of genes (typically less than 1%...

Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We examine the literature...

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem...

Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions...

Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deplo...

Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including...

Nature Biomedical Engineering - Leveraging the expertise of physicians to identify medically meaningful features in ‘counterfactual’ images produced via generative machine learning...

To explain predictions made by complex machine learning models, many feature attribution methods have been developed that assign importance scores to input features. Some recent work challenges...

The recent emergence of accurate artificial intelligence (AI) models for disease diagnosis raises the possibility that AI-based clinical decision support could substantially lower the workload of...

Explainable artificial intelligence provides an opportunity to improve prediction accuracy over standard linear models using “black box” machine learning (ML) models while still revealing insights...

삼성의 창업주 이병철의 호에서 이름을 딴 재단법인으로, 1997년 6월 설립된 호암재단(이사장 김황식)이 \'2024 삼성호암상 수상자\'를 3일 발표됐다. 학술·예술·사회봉사 등 각 분야에서 혁신적인 업적을 이룬...

As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These...

Keynote Speakers

4dslot adalah daftar slot88 dari situs toto 4d slot gacor hari ini tentunya untuk pemain judi slot online yang sedang mencari agen gampang menang berapapun dan pastinya dibayar full.

Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of individual...

Nature Biomedical Engineering - A handy go-between may soon assist authors and editors.

Calculates importance of variables and then makes recommendations

Deep neural networks offer a promising approach for capturing complex, non-linear relationships among variables. Because they require immense sample sizes, their potential has yet to be fully tapped...

Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions...

Co-located with NeurIPS. December 13-14th 2019, Vancouver, Canada. More details: https://mlcb.github.io Paper submission: https://easychair.org/my/conference?conf=mlcb2019

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains...

Chan Zuckerburg Initiative (CZI) grant awarded to Jian Ma and collaborator from the University of Washington.

Clockwise, from top left: University of Washington professor Shyam Gollakota; WRF Capital managing director Loretta Little; Amazon vice president Taha

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on...

The future use of artificial intelligence in medical contexts could be beneficial in improving efficiency, but a new University of Washington research

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively...