Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …
future potential for transforming almost all aspects of medicine. However, in many …
[HTML][HTML] An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)
M Desai, M Shah - Clinical eHealth, 2021 - Elsevier
This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …
(MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for …
[PDF][PDF] Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity
The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a
pandemic and has spread to every inhabited continent. Given the increasing caseload, there …
pandemic and has spread to every inhabited continent. Given the increasing caseload, there …
Disentangling label distribution for long-tailed visual recognition
The current evaluation protocol of long-tailed visual recognition trains the classification
model on the long-tailed source label distribution and evaluates its performance on the …
model on the long-tailed source label distribution and evaluates its performance on the …
Medical image analysis based on deep learning approach
M Puttagunta, S Ravi - Multimedia tools and applications, 2021 - Springer
Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
Background There is interest in using convolutional neural networks (CNNs) to analyze
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
A review on explainable artificial intelligence for healthcare: why, how, and when?
S Bharati, MRH Mondal… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …
medicine. Concerns have been raised about the explainability of the decisions that are …
XNOR-SRAM: In-memory computing SRAM macro for binary/ternary deep neural networks
We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that
computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural …
computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural …
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref.). To
achieve earlier cancer detection, health organizations worldwide recommend screening …
achieve earlier cancer detection, health organizations worldwide recommend screening …
[图书][B] More than a glitch: Confronting race, gender, and ability bias in tech
M Broussard - 2023 - books.google.com
When technology reinforces inequality, it's not just a glitch—it'sa signal that we need to
redesign our systems to create a more equitable world. The word “glitch” implies an …
redesign our systems to create a more equitable world. The word “glitch” implies an …