Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems

Image-based diagnostic decision support systems (DDSS) utilizing deep learning have the potential to optimize clinical workflows. However, developing DDSS requires extensive datasets with expert annotations and is therefore costly. Leveraging report contents from radiological data bases with Natural Language Processing to annotate the corresponding image data promises to replace labor-intensive manual annotation. As mining "real world" databases can introduce label noise, noise-robust training losses are of great interest. However, current noise-robust losses do not consider noise estimations that can for example be derived based on the performance of the automatic label generator used. In this study, we expand the noise-robust Deep Abstaining Classifier (DAC) loss to an Informed Deep Abstaining Classifier (IDAC) loss by incorporating noise level estimations during training. Our findings demonstrate that IDAC enhances the noise robustness compared to DAC and several state-of-the-art loss functions. The results are obtained on various simulated noise levels using a public chest X-ray data set. These findings are reproduced on an in-house noisy data set, where labels were extracted from the clinical systems of the University Hospital Bonn by a text-based transformer. The IDAC can therefore be a valuable tool for researchers, companies or clinics aiming to develop accurate and reliable DDSS from routine clinical data.

Weighted Monte Carlo augmented spherical Fourier-Bessel convolutional layers for 3D abdominal organ segmentation

Filter-decomposition-based group equivariant convolutional neural networks show promising stability and data efficiency for 3D image feature extraction. However, the existing filter-decomposition-based 3D group equivariant neural networks rely on parameter-sharing designs and are mostly limited to rotation transformation groups, where the chosen spherical harmonic filter bases consider only angular orthogonality. These limitations hamper its application to deep neural network architectures for medical image segmentation. To address these issues, this paper describes a non-parameter-sharing affine group equivariant neural network for 3D medical image segmentation based on an adaptive aggregation of Monte Carlo augmented spherical Fourier Bessel filter bases. The efficiency and flexibility of the adopted non-parameter-sharing strategy enable for the first time an efficient implementation of 3D affine group equivariant convolutional neural networks for volumetric data. The introduced spherical Bessel Fourier filter basis combines both angular and radial orthogonality for better feature extraction. The 3D image segmentation experiments on two abdominal medical image sets, BTCV and the NIH Pancreas datasets, show that the proposed methods excel the state-of-the-art 3D neural networks with high training stability and data efficiency. The code will be available at https://github.com/ZhaoWenzhao/WMCSFB.

Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network

Filter-decomposition-based group-equivariant convolutional neural networks (G-CNN) have been demonstrated to increase CNN's data efficiency and contribute to better interpretability and controllability of CNN models. However, so far filter-decomposition-based affine G-CNN methods rely on parameter sharing for achieving high parameter efficiency and suffer from a heavy computational burden. They also use a limited number of transformations and in particular ignore the shear transform in the application. In this paper, we address these problems by emphasizing the importance of the diversity of transformations. We propose a flexible and efficient strategy based on weighted filter-wise Monte Carlo sampling. In addition, we introduce shear equivariant CNN to address the highly sparse representations of natural images. We demonstrate that the proposed methods are intrinsically an efficient generalization of traditional CNNs, and we explain the advantage of bottleneck architectures used in the existing state-of-the-art CNN models such as ResNet, ResNext, and ConvNeXt from the group-equivariant perspective. Experiments on image classification and image denoising tasks show that with a set of suitable filter basis, our methods achieve superior performance to standard CNN with high data efficiency. The code will be available at https://github.com/ZhaoWenzhao/MCG_CNN.

Improving Chest X-Ray Classification by RNN-based Patient Monitoring

Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of assisting physicians in their decision making process and optimize clinical workflows, for example by prioritizing emergency patients. Most work analyzing the potential of machine learning models to classify chest X-ray images focuses on vision methods processing and predicting pathologies for one image at a time. However, many patients undergo such a procedure multiple times during course of a treatment or during a single hospital stay. The patient history, that is previous images and especially the corresponding diagnosis contain useful information that can aid a classification system in its prediction. In this study, we analyze how information about diagnosis can improve CNN-based image classification models by constructing a novel dataset from the well studied CheXpert dataset of chest X-rays. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin. We provide code to replicate the dataset creation and model training.