Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
On evaluation metrics for medical applications of artificial intelligence
Clinicians and software developers need to understand how proposed machine learning
(ML) models could improve patient care. No single metric captures all the desirable …
(ML) models could improve patient care. No single metric captures all the desirable …
Real-time polyp detection, localization and segmentation in colonoscopy using deep learning
Computer-aided detection, localization, and segmentation methods can help improve
colonoscopy procedures. Even though many methods have been built to tackle automatic …
colonoscopy procedures. Even though many methods have been built to tackle automatic …
[HTML][HTML] Surgical data science–from concepts toward clinical translation
Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …
Distribution-free, risk-controlling prediction sets
While improving prediction accuracy has been the focus of machine learning in recent years,
this alone does not suffice for reliable decision-making. Deploying learning systems in …
this alone does not suffice for reliable decision-making. Deploying learning systems in …
Catching both gray and black swans: Open-set supervised anomaly detection
Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world …
samples only, a few labeled anomaly examples are often available in many real-world …
Video polyp segmentation: A deep learning perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep
learning era. Over the years, developments in VPS are not moving forward with ease due to …
learning era. Over the years, developments in VPS are not moving forward with ease due to …
Balanced-mixup for highly imbalanced medical image classification
A Galdran, G Carneiro… - Medical Image Computing …, 2021 - Springer
Highly imbalanced datasets are ubiquitous in medical image classification problems. In such
problems, it is often the case that rare classes associated to less prevalent diseases are …
problems, it is often the case that rare classes associated to less prevalent diseases are …
Kvasir-Capsule, a video capsule endoscopy dataset
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule
endoscopy (VCE) technology. The potential lies in improving anomaly detection while …
endoscopy (VCE) technology. The potential lies in improving anomaly detection while …