Skin cancer classification with deep learning: a systematic review
Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin
lesions at an early stage could aid clinical decision-making by providing an accurate …
lesions at an early stage could aid clinical decision-making by providing an accurate …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
Do vision transformers see like convolutional neural networks?
Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …
Revisiting the calibration of modern neural networks
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe
application of neural networks. Many instances of miscalibration in modern neural networks …
application of neural networks. Many instances of miscalibration in modern neural networks …
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians
Predictive artificial intelligence (AI) systems based on deep learning have been shown to
achieve expert-level identification of diseases in multiple medical imaging settings, but can …
achieve expert-level identification of diseases in multiple medical imaging settings, but can …
What makes transfer learning work for medical images: Feature reuse & other factors
C Matsoukas, JF Haslum, M Sorkhei… - Proceedings of the …, 2022 - openaccess.thecvf.com
Transfer learning is a standard technique to transfer knowledge from one domain to another.
For applications in medical imaging, transfer from ImageNet has become the de-facto …
For applications in medical imaging, transfer from ImageNet has become the de-facto …
Exploring the limits of large scale pre-training
Recent developments in large-scale machine learning suggest that by scaling up data,
model size and training time properly, one might observe that improvements in pre-training …
model size and training time properly, one might observe that improvements in pre-training …
Does your dermatology classifier know what it doesn't know? detecting the long-tail of unseen conditions
Supervised deep learning models have proven to be highly effective in classification of
dermatological conditions. These models rely on the availability of abundant labeled training …
dermatological conditions. These models rely on the availability of abundant labeled training …
Robust and efficient medical imaging with self-supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
Improving breast cancer diagnostics with deep learning for MRI
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity
in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We …
in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We …