-
Big Self-Supervised Models Advance Medical Image Classification
Authors:
Shekoofeh Azizi,
Basil Mustafa,
Fiona Ryan,
Zachary Beaver,
Jan Freyberg,
Jonathan Deaton,
Aaron Loh,
Alan Karthikesalingam,
Simon Kornblith,
Ting Chen,
Vivek Natarajan,
Mohammad Norouzi
Abstract:
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology skin con…
▽ More
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis. This paper studies the effectiveness of self-supervised learning as a pretraining strategy for medical image classification. We conduct experiments on two distinct tasks: dermatology skin condition classification from digital camera images and multi-label chest X-ray classification, and demonstrate that self-supervised learning on ImageNet, followed by additional self-supervised learning on unlabeled domain-specific medical images significantly improves the accuracy of medical image classifiers. We introduce a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case, when available, to construct more informative positive pairs for self-supervised learning. Combining our contributions, we achieve an improvement of 6.7% in top-1 accuracy and an improvement of 1.1% in mean AUC on dermatology and chest X-ray classification respectively, outperforming strong supervised baselines pretrained on ImageNet. In addition, we show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images.
△ Less
Submitted 1 April, 2021; v1 submitted 13 January, 2021;
originally announced January 2021.
-
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Authors:
Alexander D'Amour,
Katherine Heller,
Dan Moldovan,
Ben Adlam,
Babak Alipanahi,
Alex Beutel,
Christina Chen,
Jonathan Deaton,
Jacob Eisenstein,
Matthew D. Hoffman,
Farhad Hormozdiari,
Neil Houlsby,
Shaobo Hou,
Ghassen Jerfel,
Alan Karthikesalingam,
Mario Lucic,
Yian Ma,
Cory McLean,
Diana Mincu,
Akinori Mitani,
Andrea Montanari,
Zachary Nado,
Vivek Natarajan,
Christopher Nielson,
Thomas F. Osborne
, et al. (15 additional authors not shown)
Abstract:
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predict…
▽ More
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.
△ Less
Submitted 24 November, 2020; v1 submitted 6 November, 2020;
originally announced November 2020.
-
Addressing the Real-world Class Imbalance Problem in Dermatology
Authors:
Wei-Hung Weng,
Jonathan Deaton,
Vivek Natarajan,
Gamaleldin F. Elsayed,
Yuan Liu
Abstract:
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which have low prevalence in the real world. Motivated by recent advances, we explore few-shot learning methods as well as convention…
▽ More
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which have low prevalence in the real world. Motivated by recent advances, we explore few-shot learning methods as well as conventional class imbalance techniques for the skin condition recognition problem and propose an evaluation setup to fairly assess the real-world utility of such approaches. We find the performance of few-show learning methods does not reach that of conventional class imbalance techniques, but combining the two approaches using a novel ensemble improves model performance, especially for rare classes. We conclude that ensembling can be useful to address the class imbalance problem, yet progress can further be accelerated by real-world evaluation setups for benchmarking new methods.
△ Less
Submitted 13 November, 2020; v1 submitted 8 October, 2020;
originally announced October 2020.