Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Generating semantically enriched diagnostics for radiological images using machine learning

Development of Computer Aided Diagnostic (CAD) tools to aid radiologists in pathology detection and decision making relies considerably on manually annotated images. With the advancement of deep learning techniques for CAD development, these expert annotations no longer need to be hand-crafted, however, deep learning algorithms require large amounts of data in order to generalise well. One way in which to access large volumes of expert-annotated data is through radiological exams consisting of images and reports. Using past radiological exams obtained from hospital archiving systems has many advantages: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images presents many challenges such as the difficulty in representing and generating long, unstructured textual information, accounting for spelling errors and repetition or redundancy, and the inconsistency across different annotators. In this thesis, the problem of learning to automate disease detection from radiological exams is approached from three directions. Firstly, a report generation model is developed such that it is conditioned on radiological image features. Secondly, a number of approaches are explored aimed at extracting diagnostic information from free-text reports. Finally, an alternative approach to image latent space learning from current state-of-the-art is developed that can be applied to accelerated image acquisition.Open Acces

You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray

Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods attempted this task in either fully supervised settings which demanded costly mass per-abnormality annotations, or weakly supervised settings which still lagged badly behind fully supervised methods in performance. In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa. Concretely, based on the classical teacher-student pseudo label distillation (TSD) paradigm, we additionally introduce an auxiliary report classification model, whose prediction is used for report-guided pseudo detection label refinement (RPDLR) in the primary vision detection task. Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively. To this end, we effectively incorporate the weak supervision by reports into the semi-supervised TSD pipeline. Besides the cross-modal pseudo label refinement, we further propose an intra-image-modal self-adaptive non-maximum suppression, where the pseudo detection labels generated by the teacher vision model are dynamically rectified by high-confidence predictions by the student. Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/