IterMask3D: Unsupervised Anomaly Detection and Segmentation with Test-Time Iterative Mask Refinement in 3D Brain MR

Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as 'normal'. In the testing phase, they identify patterns that deviate from this normal distribution as 'anomalies'. To learn the `normal' distribution, prevailing methods corrupt the images and train a model to reconstruct them. During testing, the model attempts to reconstruct corrupted inputs based on the learned 'normal' distribution. Deviations from this distribution lead to high reconstruction errors, which indicate potential anomalies. However, corrupting an input image inevitably causes information loss even in normal regions, leading to suboptimal reconstruction and an increased risk of false positives. To alleviate this, we propose IterMask3D, an iterative spatial mask-refining strategy designed for 3D brain MRI. We iteratively spatially mask areas of the image as corruption and reconstruct them, then shrink the mask based on reconstruction error. This process iteratively unmasks 'normal' areas to the model, whose information further guides reconstruction of 'normal' patterns under the mask to be reconstructed accurately, reducing false positives. In addition, to achieve better reconstruction performance, we also propose using high-frequency image content as additional structural information to guide the reconstruction of the masked area. Extensive experiments on the detection of both synthetic and real-world imaging artifacts, as well as segmentation of various pathological lesions across multiple MRI sequences, consistently demonstrate the effectiveness of our proposed method.

Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation

Interactive segmentation models use real-time user interactions, such as mouse clicks, as extra inputs to dynamically refine the model predictions. After model deployment, user corrections of model predictions could be used to adapt the model to the post-deployment data distribution, countering distribution-shift and enhancing reliability. Motivated by this, we introduce an online adaptation framework that enables an interactive segmentation model to continuously learn from user interaction and improve its performance on new data distributions, as it processes a sequence of test images. We introduce the Gaussian Point Loss function to train the model how to leverage user clicks, along with a two-stage online optimization method that adapts the model using the corrected predictions generated via user interactions. We demonstrate that this simple and therefore practical approach is very effective. Experiments on 5 fundus and 4 brain MRI databases demonstrate that our method outperforms existing approaches under various data distribution shifts, including segmentation of image modalities and pathologies not seen during training.

Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components

Automation of medical image interpretation could alleviate bottlenecks in diagnostic workflows, and has become of particular interest in recent years due to advancements in natural language processing. Great strides have been made towards automated radiology report generation via AI, yet ensuring clinical accuracy in generated reports is a significant challenge, hindering deployment of such methods in clinical practice. In this work we propose a quality control framework for assessing the reliability of AI-generated radiology reports with respect to semantics of diagnostic importance using modular auxiliary auditing components (AC). Evaluating our pipeline on the MIMIC-CXR dataset, our findings show that incorporating ACs in the form of disease-classifiers can enable auditing that identifies more reliable reports, resulting in higher F1 scores compared to unfiltered generated reports. Additionally, leveraging the confidence of the AC labels further improves the audit's effectiveness.

Semi-Supervised Learning for Deep Causal Generative Models

Developing models that can answer questions of the form "How would $x$ change if $y$ had been $z$?" is fundamental for advancing medical image analysis. Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that corresponding labels are available in training data. However, clinical data may not have complete records for all patients and state of the art causal generative models are unable to take full advantage of this. We thus develop, for the first time, a semi-supervised deep causal generative model that exploits the causal relationships between variables to maximise the use of all available data. We explore this in the setting where each sample is either fully labelled or fully unlabelled, as well as the more clinically realistic case of having different labels missing for each sample. We leverage techniques from causal inference to infer missing values and subsequently generate realistic counterfactuals, even for samples with incomplete labels.