Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans

Author's accepted manuscript.Available from 18/06/2021.acceptedVersio

Uncertainty quantification in medical image segmentation with normalizing flows

Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probability scores. In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow). The basic idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. With this we show that the quality and diversity of samples obtained from our conditional generative model is enhanced. Performance of our model, which we call cFlow Net, is evaluated on two medical imaging datasets demonstrating substantial improvements in both qualitative and quantitative measures when compared to a recent cVAE based model.Comment: 12 pages. Accepted to be presented at 11th International Workshop on Machine Learning in Medical Imaging. Source code will be updated at https://github.com/raghavian/cFlo

Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis

Author's accepted manuscript.Available from 28/11/2021.acceptedVersio

Learning Visual Context by Comparison

Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, but one of the most important characteristics is still missing: the necessity of comparison between related regions in an image. In this paper, we present Attend-and-Compare Module (ACM) for capturing the difference between an object of interest and its corresponding context. We show that explicit difference modeling can be very helpful in tasks that require direct comparison between locations from afar. This module can be plugged into existing deep learning models. For evaluation, we apply our module to three chest X-ray recognition tasks and COCO object detection & segmentation tasks and observe consistent improvements across tasks. The code is available at https://github.com/mk-minchul/attend-and-compare.Comment: ECCV 2020 spotlight pape

Robust Fusion of Probability Maps

International audienceThe fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight properly the combination of maps in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion. We introduce a novel approach to jointly estimate a reliable consensus map and assess the production of outliers and the confidence in each rater. Our probabilistic model is based on Student's t-distributions allowing local estimates of raters' performances. The introduction of bias and spatial priors leads to proper rater bias estimates and a control over the smoothness of the consensus map. Image intensity information is incorporated by geodesic distance transform for binary masks. Finally, we propose an approach to cluster raters based on variational boosting thus producing possibly several alternative consensus maps. Our approach was successfully tested on the MICCAI 2016 MS lesions dataset, on MR prostate delineations and on deep learning based segmentation predictions of lung nodules from the LIDC dataset

Mapping LIDC, RadLex™, and Lung Nodule Image Features

Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists’ interpretations of images. The Lung Image Database Consortium (LIDC) has provided a database of lung nodule images and associated radiologist ratings in an effort to provide images to aid in the analysis of computer-aided tools. Likewise, the Radiological Society of North America has developed a radiological lexicon called RadLex. As such, the goal of this paper is to investigate the feasibility of associating LIDC characteristics and terminology with RadLex terminology. If matches between LIDC characteristics and RadLex terms are found, probabilistic models based on image features may be used as decision-based rules to predict if an image or lung nodule could be characterized or classified as an associated RadLex term. The results of this study were matches for 25 (74%) out of 34 LIDC terms in RadLex. This suggests that LIDC characteristics and associated rating terminology may be better conceptualized or reduced to produce even more matches with RadLex. Ultimately, the goal is to identify and establish a more standardized rating system and terminology to reduce the subjective variability between radiologist annotations. A standardized rating system can then be utilized by future researchers to develop automatic annotation models and tools for computer-aided decision systems

Visual interpretation with three-dimensional annotations (VITA): Three-dimensional image interpretation tool for radiological reporting

10.1007/s10278-013-9624-5Journal of Digital Imaging27149-57JDIM