pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Alzheimer disease’s structural connectivity through explainable AI
Authors: Achraf Essemlali, Etienne St-Onge, Jean Christophe Houde, Pierre Marc Jodoin, Maxime Descoteaux
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Diffusion  MRI

In the following work, we use a modified version of deep BrainNet convolutional neural network (CNN) trained on the diffusion weighted MRI (DW-MRI) tractography connectomes of patients with Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) to better understand the structural connectomics of that disease. We show that with a relatively simple connectomic BrainNetCNN used to classify brain images and explainable AI techniques, one can underline brain regions and their connectivity involved in AD. Results reveal that the connected regions with high structural differences between groups are those also reported in previous AD literature. Our findings support that deep learning over structural connectomes is a powerful tool to leverage the complex structure within connectomes derived from diffusion MRI tractography. To our knowledge, our contribution is the first explainable AI work applied to structural analysis of a degenerative disease.
Multitask radiological modality invariant landmark localization using deep reinforcement learning
Authors: Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning techniques are increasingly being developed for several applications in radiology, for example landmark and organ localization with segmentation. However, these applications to date have been limited in nature, in that, they are restricted to just a single task e.g. localization of tumors or to a specific organ using supervised training by an expert. As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ. This would be both space and computationally expensive. In addition, the true potential of deep learning methods in radiology can only be achieved when the model is adaptable and generalizable to multiple different tasks. To that end, we have developed and implemented a multitask modality invariant deep reinforcement learning framework (MIDRL) for landmark localization and segmentation in radiological applications. MIDRL was evaluated using a diverse data set containing multiparametric MRIs (mpMRI) acquired from different organs and with different imaging parameters. A 2D single agent model was trained to localize six different anatomical structures throughout the body, including, knee, trochanter, heart, kidney, breast nipple, and prostate across T1 weighted, T2 weighted, Dynamic Contrast Enhanced (DCE), Diffusion Weighted Imaging (DWI), and DIXON MRI sequences obtained from twenty-four breast, eight prostate, and twenty five whole body mpMRIs. Additionally, a 3D multi-agent model was trained to localize knee, trochanter, heart, and kidney in the whole body mpMRIs. The trained MIDRL framework produced excellent accuracy in localizing each of the anatomical landmarks. In conclusion, we developed a multitask deep reinforcement learning framework and demonstrated MIDRL’s potential towards the development of a general AI for a radiological decision support system.
Deep learning-based retinal vessel segmentation with cross-modal evaluation
Authors: Luisa Sanchez Brea, Danilo Andrade De Jesus, Stefan Klein, Theo van Walsum
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Deep Learning, Retinal Vessel Segmentation, Sensitivity, Specificity

This work proposes a general pipeline for retinal vessel segmentation on en-face images. The main goal is to analyse if a model trained in one of two modalities, Fundus Photography (FP) or Scanning Laser Ophthalmoscopy (SLO), is transferable to the other modality accurately. This is motivated by the lack of development and data available in en-face imaging modalities other than FP. FP and SLO images of four and two publicly available datasets, respectively, were used. First, the current approaches were reviewed in order to define a basic pipeline for vessel segmentation. A state-of-art deep learning architecture (U-net) was used, and the effect of varying the patch size and number of patches was studied by training, validating, and testing on each dataset individually. Next, the model was trained in either FP or SLO images, using the available datasets for a given modality combined. Finally, the performance of each network was tested on the other modality. The models trained on each dataset showed a performance comparable to the state-of-the art and to the inter-rater reliability. Overall, the best performance was observed for the largest patch size (256) and the maximum number of overlapped images in each dataset, with a mean sensitivity, specificity, accuracy, and Dice score of 0.89$\pm$0.05, 0.95$\pm$0.02, 0.95$\pm$0.02, and 0.73$\pm$0.07, respectively. Models trained and tested on the same modality presented a sensitivity, specificity, and accuracy equal or higher than 0.9. The validation on a different modality has shown significantly better sensitivity and Dice on those trained on FP.
Direct estimation of fetal head circumference from ultrasound images based on regression CNN
Authors: Jing Zhang, Caroline Petitjean, Pierre Lopez, Samia Ainouz
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: regression

The measurement of fetal head circumference (HC) is performed throughout the pregnancy as a key biometric to monitor fetus growth. This measurement is performed on ultrasound images, via the manual fitting of an ellipse. The operation is operator-dependent and as such prone to intra and inter-variability error. There have been attempts to design automated segmentation algorithms to segment fetal head, especially based on deep encoding-decoding architectures. In this paper, we depart from this idea and propose to leverage the ability of convolutional neural networks (CNN) to directly measure the head circumference, without having to resort to handcrafted features or manually labeled segmented images. The intuition behind this idea is that the CNN will  learn itself to localize and identify the head contour. Our approach is experimented on the public HC18 dataset, that contains images of all trimesters of the pregnancy. We investigate various architectures and three losses suitable for regression. While room for improvement is left, encouraging results show that it might be possible in the future to directly estimate the HC - without the need for a large dataset of manually segmented ultrasound images. This approach might be extended to other applications where segmentation is just an intermediate step to the computation of biomarkers.
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Authors: Jasper Linmans, Jeroen van der Laak, Geert Litjens
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Successful clinical implementation of deep learning in medical imaging depends, in part, on the reliability of the predictions. Specifically, the system should be accurate for classes seen during training while providing calibrated estimates of uncertainty for abnormalities and unseen classes. To efficiently estimate predictive uncertainty, we propose the use of multi-head convolutional neural networks (M-heads). We compare its performance to related and more prevalent approaches, such as deep ensembles, on the task of out-of-distribution (OOD) detection. To this end, we evaluate models, trained to discriminate normal lymph node tissue from breast cancer metastases, on lymph nodes containing lymphoma. We show the ability to discriminate between the in-distribution lymph node tissue and lymphoma by evaluating the AUROC based on the uncertainty signal. Here, the best performing multi-head CNN (91.7) outperforms both Monte Carlo dropout (88.3) and deep ensembles (86.8). Furthermore, we show that the meta-loss function of M-heads improves OOD detection in terms of AUROC from 87.4 to 88.7.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective
Authors: Ahmad B Qasim, Ivan Ezhov, Suprosanna Shit, Oliver Schoppe, Johannes Paetzold, Anjany Sekuboyina, Florian Kofler, Jana Lipkova, Hongwei Li, Bjoern Menze
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Data Augmentation, Segmentation

Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmentation protocol based on generative adversarial networks. The networks are conditioned at a pixel-level (segmentation mask) and at a global-level information (acquisition environment or lesion type). Such conditioning provides immediate access to the image-label pairs while controlling class specific appearance of the synthesized images. To stimulate synthesis of the features relevant for the segmentation task, an additional passive player in a form of segmentor is introduced into the adversarial game.
We validate the approach on two medical datasets: BraTS, ISIC. By controlling the class distribution through injection of synthetic images into the training set we achieve control over the accuracy levels of the datasets' classes.
Accurate Detection of Out of Body Segments in Surgical Video using Semi-Supervised Learning
Authors: Maya Zohar, Omri Bar, Daniel Neimark, Gregory D. Hager, Dotan Asselmann
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Large labeled datasets are an important precondition for deep learning models to achieve state-of-the-art results in computer vision tasks. In the medical imaging domain, privacy concerns have limited the rate of adoption of artificial intelligence methodologies into clinical practice. To alleviate such concerns, and increase comfort levels while sharing and storing surgical video data, we propose a high accuracy method for rapid removal and anonymization of out-of-body and non-relevant surgery segments. Training a deep model to detect out-of-body and non-relevant segments in surgical videos requires suitable labeling. Since annotating surgical videos with per-second relevancy labeling is a tedious task, our proposed framework initiates the learning process from a weakly labeled noisy dataset and iteratively applies Semi-Supervised Learning (SSL) to re-annotate the training data samples. Evaluating our model, on an independent test set, shows a mean detection accuracy of above 97% after several training-annotating iterations. Since our final goal is achieving out-of-body segments detection for anonymization, we evaluate our ability to detect these segments at a high demanding recall of 97%, which leads to a precision of 83.5%. We believe this approach can be applied to similar related medical problems, in which only a coarse set of relevancy labels exists, currently limiting the possibility for supervision training.
Adversarial Domain Adaptation for Cell Segmentation
Authors: Mohammad Minhazul Haq, Junzhou Huang
Date: 2020-01-29 00:00:00
Proceeding: MIDL 2019 7
Tasks: Cell Segmentation, Domain Adaptation, Segmentation, Semi-supervised Domain Adaptation, Unsupervised Domain Adaptation

To successfully train a cell segmentation network in fully-supervised manner for a particular type of organ or cancer, we need the dataset with ground-truth annotations. However, high unavailability of such annotated dataset and tedious labeling process enforce us to discover a way for training with unlabeled dataset. In this paper, we propose a network named CellSegUDA for cell segmentation on the unlabeled dataset (target domain). It is achieved by applying unsupervised domain adaptation (UDA) technique with the help of another labeled dataset (source domain) that may come from other organs or sources. We validate our proposed CellSegUDA on two public cell segmentation datasets and obtain significant improvement as compared with the baseline methods. Finally, considering the scenario when we have a small number of annotations available from the target domain, we extend our work to CellSegSSDA, a semi-supervised domain adaptation (SSDA) based approach. Our SSDA model also gives excellent results which are quite close to the fully-supervised upper bound in target domain.
Comparing Objective Functions for Segmentation and Detection of Microaneurysms in Retinal Images
Authors: Jakob Kristian Holm Andersen, Thiusius Rajeeth Savarimuthu, Jakob Grauslund
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Retinal microaneurysms (MAs) are the earliest signs of diabetic retinopathy (DR) which is the leading cause of blindness among the working aged population in the western world. Detection of MAs present a particular challenge as MA pixels account for less than 0.5% of the retinal image. In deep neural networks the learning process can be adversely affected by imbalance which introduces a bias towards the most well represented class. Recently, a number of objective functions have been proposed as alternatives to the standard Crossentropy (CE) loss in efforts to combat this problem. In this work we investigate the influence of the network objective during optimization by comparing Residual U-nets trained for segmentation of MAs in retinal images using six different objective functions; weighted and unweighted CE, Dice loss, weighted and unweighted Focal loss and Focal Tversky loss. We also perform test with the CE objective using a more complex model. Three networks with different seeds are trained for each objective function using optimized hyper-parameter settings on a dataset of 382 images with pixel level annotations for MAs. Instance level MA detection performance is evaluated with the average free response receiver operator characteristic (FROC) score calculated as the mean sensitivity at seven average false positives per image thresholds on 80 test images. The image level MA detection performance and detection of low levels of DR is evaluated with bootstrapped AUC scores on the same images and a separate test set of 1287 images. Significance test for image level detection accuracy (α= 0.05) is performed  using Cochran's Q and McNemar's test. Segmentation performance is evaluated with the average pixel precision (AP) score. For instance level detection and pixel segmentation we perform repeated measures ANOVA with Post-Hoc tests.
Results: Losses based on the CE index perform significantly better than the Dice and Focal Tversky loss for instance level detection and pixel segmentation. The highest FROC score of 0.5448 (±0.0096) and AP of 0.4888 (±0.0196) is achieved using weighted CE. For all objectives excluding the Focal Tversky loss (AUC = 0.5) there is no significant difference for image level detection accuracy on the 80 image test set. The highest AUC of 0.993 (95% CI:0.980 - 1.0) is achieved using the Focal loss. For detection of mild DR on the set of 1287 images there is a significant difference between model objectives  (p= 2.87e−12). An AUC of 0.730 (95% CI: 0.683 - 0.745 is achieved using the complex model with CE. Using the Focal Tversky objective we fail to detect any MAs on both instance and image level.
Conclusion: Our results suggest that it is important to benchmark new losses against the CE and Focal loss functions, as we achieve similar or better results in our test using these objectives.
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Authors: Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp, Guido Buonincontri, Miguel Molina-Romero, Anjany Sekuboyina, Diana Waldmannstetter, Jonathan Dannenberg, Sebastian Endt, Alberto Merola, Joseph R. Whittaker, Valentina Tomassini, Michela Tosetti, Derek K. Jones, Bjoern H. Menze, Marion I. Menzel
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions.
Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans
Authors: Vincent Andrearczyk, Valentin Oreiller, Martin Vallières, Joel Castelli, Hesham Elhalawani, Sarah Boughdad, Mario Jreige, John O. Prior and Adrien Depeursinge
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation

Radiomics, the prediction of disease characteristics using quantitative image biomarkers from medical images, relies on expensive manual annotations of Regions of Interest (ROI) to focus the analysis. In this paper, we propose an automatic segmentation of Head and Neck (H&N) tumors and nodal metastases from FDG-PET and CT images. A fully-convolutional network (2D and 3D V-Net) is trained on PET-CT images using ground truth ROIs that were manually delineated by radiation oncologists for 202 patients. The results show the complementarity of the two modalities with a statistically significant improvement from 48.7% and 58.2% Dice Score Coefficients (DSC) with CT- and PET-only segmentation respectively, to 60.6% with a bimodal late fusion approach. We also note that, on this task, a 2D implementation slightly outperforms a similar 3D design (60.6% vs 59.7% for the best results respectively).
Siamese Content Loss Networks for Highly Imbalanced Medical Image Segmentation
Authors: Brandon Mac, Alan R. Moody, April Khademi
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Transfer Learning

Automatic segmentation of white matter hyperintensities (WMHs) in magnetic resonance imaging (MRI) remains highly sought after due to the potential to streamline and alleviate clinical workflows. WMHs are small relative to whole acquired volume, which leads to class imbalance issues, and instability during the training process of many deep learning based solutions. To address this, we propose a method which is robust to effects of class imbalance, through incorporating multi-scale information in the training process. Our method consists of training an encoder-decoder neural network utilizing a Siamese network as an auxiliary loss function. These Siamese networks take in pairs of image pairs, input images masked with ground truth labels, and input images masked with predictions, and computes multi-resolution feature vector representations and provides gradient feedback in the form of a L2 norm. We leverage transfer learning in our Siamese network, and present positive results without need to further train. It was found these methods are more robust for training segmentation neural networks and provide greater generalizability. Our method was cross-validated on multi-center data, yielding significant overall agreement with manual annotations.
Improving the Ability of Deep Neural Networks to Use Information From Multiple Views in Breast Cancer Screening
Authors: Nan Wu, Stanisław Jastrzębski, Jungkyu Park, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Medical Diagnosis

In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles. Inspired by this, we seek to improve the performance of deep neural networks applied to this task by encouraging the model to use information from both views of the breast. First, we took a closer look at the training process and observed an imbalance between learning from the two views. In particular, we observed that layers processing one of the views have parameters with larger gradients in magnitude, and contribute more to the overall loss reduction. Next, we tested several methods targeted at utilizing both views more equally in training. We found that using the same weights to process both views, or using modality dropout, leads to a boost in performance. Looking forward, our results indicate improving learning dynamics as a promising avenue for improving utilization of multiple views in deep neural networks for medical diagnosis.
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning
Authors: Chensu Xie, Hassan Muhammad, Chad M. Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, Thomas J. Fuchs
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, whole slide images

An emerging technology in cancer care and research is the use of histopathology whole slide images (WSI). Leveraging computation methods to aid in WSI assessment poses unique challenges. WSIs, being extremely high resolution giga-pixel images, cannot be directly processed by convolutional neural networks (CNN) due to huge computational cost. For this reason, state-of-the-art methods for WSI analysis adopt a two-stage approach where the training of a tile encoder is decoupled from the tile aggregation. This results in a trade-off between learning diverse and discriminative features. In contrast, we propose end-to-end part learning (EPL) which is able to learn diverse features while ensuring that learned features are discriminative. Each WSI is modeled as consisting of $k$ groups of tiles with similar features, defined as parts. A loss with respect to the slide label is backpropagated through an integrated CNN model to $k$ input tiles that are used to represent each part. Our experiments show that EPL is capable of clinical grade prediction of prostate and basal cell carcinoma. Further, we show that diverse discriminative features produced by EPL succeeds in multi-label classification of lung cancer architectural subtypes. Beyond classification, our method provides rich information of slides for high quality clinical decision support.