MEMoRIAL

2024

1. Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images

   Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, Valerie Krug, Rupali Khatun, Rahul Mishra, Nirja Desai, Petia Radeva, Georg Rose, Sebastian Stober, Oliver Speck, and Andreas Nürnberger

   Journal of Imaging, Feb 2024

   Abs DOI Bib

   The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods—occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT—and using a global technique—neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model.

   @article{Chatterjee2024,
     author = {Chatterjee, Soumick and Saad, Fatima and Sarasaen, Chompunuch and Ghosh, Suhita and Krug, Valerie and Khatun, Rupali and Mishra, Rahul and Desai, Nirja and Radeva, Petia and Rose, Georg and Stober, Sebastian and Speck, Oliver and Nürnberger, Andreas},
     journal = {Journal of Imaging},
     title = {Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images},
     year = {2024},
     issn = {2313-433X},
     month = feb,
     number = {2},
     pages = {45},
     volume = {10},
     doi = {10.3390/jimaging10020045},
     publisher = {MDPI AG}
   }

2023

1. PI-RADS v2 Compliant Automated Segmentation of Prostate Zones Using co-training Motivated Multi-task Dual-Path CNN

   Arnab Das, Suhita Ghosh, and Sebastian Stober

   2023

   Abs DOI Bib

   The detailed images produced by Magnetic Resonance Imaging (MRI) provide life-critical information for the diagnosis and treatment of prostate cancer. To provide standardized acquisition, interpretation and usage of the complex MRI images, the PI-RADS v2 guideline was proposed. An automated segmentation following the guideline facilitates consistent and precise lesion detection, staging and treatment. The guideline recommends a division of the prostate into four zones, PZ (peripheral zone), TZ (transition zone), DPU (distal prostatic urethra) and AFS (anterior fibromuscular stroma). Not every zone shares a boundary with the others and is present in every slice. Further, the representations captured by a single model might not suffice for all zones. This motivated us to design a dual-branch convolutional neural network (CNN), where each branch captures the representations of the connected zones separately. Further, the representations from different branches act complementary to each other at the second stage of training, where they are fine-tuned through an unsupervised loss. The loss penalises the difference in predictions from the two branches for the same class. We also incorporate multi-task learning in our framework to further improve the segmentation accuracy. The proposed approach improves the segmentation accuracy of the baseline (mean absolute symmetric distance) by 7.56%, 11.00%, 58.43% and 19.67% for PZ, TZ, DPU and AFS zones respectively.

   @article{Das2023a,
     author = {Das, Arnab and Ghosh, Suhita and Stober, Sebastian},
     title = {PI-RADS v2 Compliant Automated Segmentation of Prostate Zones Using co-training Motivated Multi-task Dual-Path CNN},
     year = {2023},
     copyright = {Creative Commons Attribution 4.0 International},
     doi = {10.48550/ARXIV.2309.12970},
     publisher = {arXiv}
   }

2022

1. Towards Patient Specific Reconstruction Using Perception-Aware CNN and Planning CT as Prior

   Suhita Ghosh, Philipp Ernst, Georg Rose, Andreas Nurnberger, and Sebastian Stober

   In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Mar 2022

   Abs DOI Bib

   The C-arm Cone-Beam Computed Tomography (CBCT) increasingly plays a major role in interventions and radiotherapy. However, the slow data acquisition and high dose hinder its predominance in the clinical routine. To overcome the high-dose issue, various protocols such as sparse-view have been proposed, where a subset of projections is acquired over increased angular steps. However, applying the standard reconstruction algorithms to datasets obtained from such protocols results in volumes with severe streaking artifacts. Further, the presence of surgical instruments worsens the quality and make the reconstructions clinically useless. High-quality pre-operative CT scans are usually acquired for diagnosis and intervention planning, which contain the required high-resolution details of the body part. In this work, we propose a deep learning-based method that incorporates the planning CT along with the sparse-sampled interventional CBCT of the same subject to produce high-quality reconstructions containing the surgical instrument. We also propose a perception-aware loss for the task, which facilitates the model to capture the surgical instrument precisely, compared to the pixelwise mean squared error (MSE). The model using the planning CT and trained with pixelwise MSE loss improves the soft-tissue contrast and the reconstruction quality (mean PSNR) from 27.46 dB to 32.89 dB. The perception-aware loss further improves the reconstruction quality statistically significantly to 36.14 dB.

   @inproceedings{Ghosh2022,
     author = {Ghosh, Suhita and Ernst, Philipp and Rose, Georg and Nurnberger, Andreas and Stober, Sebastian},
     booktitle = {2022 {IEEE} 19th International Symposium on Biomedical Imaging ({ISBI})},
     title = {Towards Patient Specific Reconstruction Using Perception-Aware {CNN} and Planning {CT} as Prior},
     year = {2022},
     month = mar,
     publisher = {{IEEE}},
     doi = {10.1109/isbi52829.2022.9761462}
   }

2021

1. Perception-Aware Losses Facilitate CT Denoising and Artifact Removal

   Suhita Ghosh, Andreas Krug, Georg Rose, and Sebastian Stober

   In 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), Sep 2021

   Abs DOI Bib

   The concerns over radiation-related health risks associated with the increasing use of computed tomography (CT) have accelerated the development of low-dose strategies. There is a higher need for low dosage in interventional applications as repeated scanning is performed. However, using the noisier and under-sampled low-dose datasets, the standard reconstruction algorithms produce low-resolution images with severe streaking artifacts. This adversely affects the CT assisted interventions. Recently, variational autoencoders (VAEs) have achieved state of-the-art results for the reconstruction of high fidelity images. The existing VAE approaches typically use mean squared error (MSE) as the loss, because it is convex and differentiable. However, pixel wise MSE does not capture the perceptual quality difference between the target and model predictions. In this work, we propose two simple but effective MSE based perception aware losses, which facilitate a better reconstruction quality. The proposed losses are motivated by perceptual fidelity measures used in image quality assessment. One of the losses involves calculation of the MSE in the spectral domain. The other involves calculation of the MSE in the pixel space and the Laplacian of Gaussian transformed domain. We use a hierarchical vector-quantized VAE equipped with the perception-aware losses for the artifact removal task. The best performing perception-aware loss improves the structural similarity index measure (SSIM) from 0.74 to 0.80. Further, we provide an analysis of the role of the pertinent components of the architecture in the denoising and artifact removal task.

   @inproceedings{Ghosh2021,
     author = {Ghosh, Suhita and Krug, Andreas and Rose, Georg and Stober, Sebastian},
     booktitle = {2021 {IEEE} 2nd International Conference on Human-Machine Systems ({ICHMS})},
     title = {Perception-Aware Losses Facilitate {CT} Denoising and Artifact Removal},
     year = {2021},
     month = sep,
     publisher = {{IEEE}},
     doi = {10.1109/ichms53169.2021.9582444}
   }

2. Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond

   Anneke Meyer, Suhita Ghosh, Daniel Schindele, Martin Schostak, Sebastian Stober, Christian Hansen, and Marko Rak

   Artificial Intelligence in Medicine, Jun 2021

   Abs DOI Bib

   Various convolutional neural network (CNN) based concepts have been introduced for the prostate’s automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the anterior fibromuscular stroma (AFS), the task becomes more challenging and has not yet been solved at the level of human performance. One reason might be the insufficient amount of labeled data for supervised training. Therefore, we propose to apply a semi-supervised learning (SSL) technique named uncertainty-aware temporal self-learning (UATS) to overcome the expensive and time-consuming manual ground truth labeling. We combine the SSL techniques temporal ensembling and uncertainty-guided self-learning to benefit from unlabeled images, which are often readily available. Our method significantly outperforms the supervised baseline and obtained a Dice coefficient (DC) of up to 78.9%, 87.3%, 75.3%, 50.6% for TZ, PZ, DPU and AFS, respectively. The obtained results are in the range of human inter-rater performance for all structures. Moreover, we investigate the method’s robustness against noise and demonstrate the generalization capability for varying ratios of labeled data and on other challenging tasks, namely the hippocampus and skin lesion segmentation. UATS achieved superiority segmentation quality compared to the supervised baseline, particularly for minimal amounts of labeled data.

   @article{Meyer2021,
     author = {Meyer, Anneke and Ghosh, Suhita and Schindele, Daniel and Schostak, Martin and Stober, Sebastian and Hansen, Christian and Rak, Marko},
     journal = {Artificial Intelligence in Medicine},
     title = {Uncertainty-aware temporal self-learning ({UATS}): Semi-supervised learning for segmentation of prostate zones and beyond},
     year = {2021},
     month = jun,
     pages = {102073},
     volume = {116},
     doi = {10.1016/j.artmed.2021.102073},
     publisher = {Elsevier {BV}}
   }