pwc-archive/papers-with-abstracts huggingface dataset:
Model Averaging and Augmented Inference for Stable Echocardiography Segmentation using 2D ConvNets
Authors: Joshua V. Stough
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Data Augmentation

The automatic segmentation of heart substructures in 2D echocardiography images is a goal common to both clinicians and researchers. Convolutional neural networks (CNNs) have recently shown the best average performance. However, on the rare occasions that a trained CNN fails, it can fail spectacularly. To mitigate these errors, in this work we develop and validate two easily implementable schemes for regularizing performance in 2D CNNs: model averaging and augmented inference. Model averaging involves training multiple instances of a CNN with data augmentation over a sampled training set. Augmented inference involves accumulating network output over augmentations of the test image. Using the recently released CAMUS echocardiography dataset, we show significant incremental improvement in outlier performance over the baseline model. These encouraging results must still be validated against independent clinical data.
Which MOoD Methods work? A Benchmark of Medical Out of Distribution Detection
Authors: Tianshi Cao, David Yu-Tung Hui, Chinwei Huang, Joseph Paul Cohen
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Medical Diagnosis, Out-of-Distribution Detection

There is a rise in the use of deep learning for automated medical diagnosis, most notably in  medical  imaging.   Such  an  automated  system  uses  a  set  of  images  from  a  patient  to diagnose whether they have a disease.  However, systems trained for one particular domain of images cannot be expected to perform accurately on images of a different domain.  These images should be filtered out by an Out-of-Distribution Detection (OoDD) method prior to diagnosis.  This paper benchmarks popular OoDD methods in three domains of medical imaging:  chest x-rays, fundus images and histology slides.  Despite methods yielding good results on all three domains, they fail to recognize images close to the training distribution.
Poolability and Transferability in CNN. A Thrifty Approach
Authors: Jonathan Kobold, Vincent Vigneron, Hichem Maaref, Elmar Lang, Ana Maria Tomé
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation, Semantic Segmentation

The current trend in deep learning models for semantic segmentation are ever increasing model sizes. These large models need huge data-sets to be trained properly. However medical applications often offer only  small data-sets available and require smaller models. A large part of these models' parameters is due to their multi-resolution approach for increasing the receptive field, i.e. alternating convolution and pooling layers for feature extraction. In this work an alternative parameter free approach is proposed to increase the receptive field. This significantly reduces the number of parameters needed in  semantic segmentation models and allows them to be trained on smaller data-sets.
Hybrid Apparent Diffusion Coefficient (HADC) Map
Authors: Samira Masoudi, Stephanie A. Harmon, Stephanie Walker, Sherif Mehralivand, Ulas Bagci, Peter L. Choyke, Baris Turkbey
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Generative Adversarial Network

Multiparametric MRI (mpMRI) is an established framework for prostate cancer assessment which includes T2-weighted magnetic resonance (T2w-MR) and diffusion weighted (DW) sequences. Low quality of Apparent Diffusion Coefficient (ADC) maps from the diffusion sequence can hinder such clinical assessment. Herein, we propose to generate Hybrid ADC (HADC) maps from high-quality T2w-MRI using “lesion-aware cycle-consistent generative adversarial network (LA-CGAN)”. Our produced HADC maps contain anatomical information from T2w-MR, and high intra-prostatic contrast of cancerous vs normal tissue, similar to the acquired ADC. Initial results have satisfied the expert radiologist in producing HADC. This work can considerably improve the quality of mpMRI combined assessment for prostate cancer detection.
Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling
Authors: Yuanhan Mo, Shuo Wang, Chengliang Dai, Zhongzhao Teng, Wenjia Bai, Yike Guo
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Informativeness, Medical Image Analysis

Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain.  During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance.  However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples. In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation. To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process. To evaluate the proposed idea, we perform an experiment on a medical image dataset IVUS for biophysical simulation task.
Multi-view Framework for Histomorphologic Classification
Authors: Stephanie A Harmon, Samira Masoudi, Thomas Sanford, Sherif Mehralivand, Stephanie Walker, Peter Choyke, Brad Wood, Peter Pinto, Jesse McKenney, Baris Turkbey
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Classification

Current routine histopathologic evaluation of prostate cancer does not fully account for some individual morphology patterns associated with poor outcome.  Pathologists evaluate and score morphology across multiple magnifications, motivating deep learning methods to incorporate various resolutions.  We have evaluated a proof-of-concept multi-view framework to classify high risk morphology architectures that does not rely on ensemble-based techniques of multi-magnification models.
Applying Machine Learning Algorithms for Kidney Disease Diagnosis
Authors: Yenatfanta Shifferaw Bayleyegn, Tariku Adane Gelaw
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: BIG-bench Machine Learning

Kidney disease is a ‘silent killer’; it usually develops over time, and many people do not know they have it until it is very far along. Chronic Kidney Disease (represents a major public health problem in both developed and developing countries. In this rese arch, three different classification algorithms have been used in order to evaluate the occurrence of ckd on a dataset collected from the UCI Repository which holds 400 samples with 25 attributes. By filtering out t he top 14 features from the 2 4 input variables which show high score of dependability, the experimental results manifest t hat Multi Layer Perceptron (MLP) works best for both normalized and unnormalized features yielding an accuracy of 100%.
A hierarchical fusion framework integrating random projection-based classifiers: application in head and neck squamous carcinoma cancer
Authors: Tianlan Mo, Chao Zheng
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Decision Making, Diversity, Survival Prediction

Ensemble methods achieves better performance than single classifier model. Classifier diversity and fusion architecture are equally important for building a successful multi-classifier system. In this study, we introduced random projection to obtain the required classifier diversity and then proposed a hierarchical framework, namely a novel hierarchical fusion integrating random projection diversified classifiers (HFRPC). The proposed hierarchical fusion scheme was validated on survival prediction of head and neck squamous carcinoma cancer (HNSCC). Experimental results have demonstrated the superiority of the proposed HFRPC framework over the base classifier member and the state-of-the-art benchmark ensemble methods, rendering it a potential tool to assist medical decision making in the practical clinical setting.
Towards Multiple Enhancement Styles Generation in Mammography
Authors: Shilin Chen, Ji He, Jianhua Ma
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Mammography is a well-established imaging modality for early detection and diagnosis of breast
cancer. The raw detector-obtained mammograms are difficult for radiologists to diagnose due to the
similarity between normal tissues and potential lesions in the attenuation level and thus mammogram
enhancement (ME) is significantly necessary. However, the enhanced mammograms obtained
with different mammography devices can be diverse in visualization due to different enhancement
algorithms adopted in these mammography devices. Different styles of enhanced mammograms
can provide different information of breast tissue and lesion, which might help radiologists to
screen breast cancer better. In this paper, we present a deep learning (DL) framework to achieve
multiple enhancement styles generation for mammogram enhancement. The presented DL framework
is denoted as DL-ME for simplicity. Specifically, the presented DL-ME is implemented with
a multi-scale cascaded residual convolutional neural network (MSC-ResNet), in which the output
in the coarser scale is used as a part of inputs in the finer scale to achieve optimal ME performance.
In addition, a switch map is input into the DL-ME model to control the enhancement style of the
outputs. To reveal the multiple enhancement styles generation ability of DL-ME for mammograms,
clinical mammographic data from mammography devices of three different manufacturers are used
in the work. The results show that the quality of the mammograms generated by our framework can
reach the level of clinical diagnosis and enhanced mammograms with different styles can provide
more information, which can help radiologists to efficiently screen breast cancers.
End-to-End Breast Mass Classification on Digital Breast Tomosynthesis
Authors: Chao Zheng, Tianlan Mo
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Automatic classification of the masses in digital breast tomosynthesis(DBT) is still a big challenge and play a crucial role to assist radiologists for accurate diagnosis.In this paper,we develop a End-to-End multi-scale multi-level features fusion Network (EMMFFN) model for breast mass classication using DBT. Three multifaceted representations of the breast mass (gross mass, overview, and mass background) are extracted from the ROIs and then, fed into the EMMFFN model at the same time to generated three sets of feature map.The three feature maps are finally fused at the feature level to generate the final prediction.Our result show that the EMMFFN model achieves a breast mass classification AUC of 85.09%,which was superior to the single submodel who only use one aspect of patch.
Effect of GAN augmented dataset size on deep learning-based ultrasound bone segmentation model training
Authors: Asaduz Zaman, Hyunhee Bang, Chul-woo Park, Ilhyung Park, Sanghyun Joung
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Data Augmentation, Deep Learning

Recently, ultrasound imaging is increasingly being used as intra-operative imaging modality in osteolytic bone surgery due to its cost-effectiveness and radiation-free nature. Deep learning approaches have shown remarkable success in segmenting bone surface from ultrasound images. However, limited training dataset size has always hindered the success of deep learning approaches, which is especially evident in ultrasound bone segmentation since there is no publicly available quality dataset. To resolve the issue, in addition to standard data augmentation approaches, generative adversarial networks (GANs) have recently been used for generating augmented training samples. Although the addition of the generative approach in data augmentation is observed to have a positive effect on deep learning architecture's performance, the question about the effect of using a multi-fold of GAN generated training dataset remains unanswered. In this work, we have generated 14 fold of GAN-augmented training dataset and evaluated the performance of the network for successively increased dataset size. Our test results show that although the use of the GAN-augmented training dataset in addition to standard augmentation approaches helps perform the network better, the addition of multi-fold GAN-augmented datasets has no noticeable performance gain.
Chest X-Ray Pneumothorax Segmentation with the Multistep Post-Processing
Authors: Vladimir Groza, Artur Kuzin
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this work we present recent results of the pneumothorax segmentation from the chest X-ray images. Pneumothorax may appear in case of dull chest injury, as a continuation of hidden problems with the lungs, or even more there could be no reason at all for finding. In several situations, lung collapse can turn out as serious threat to life.
We propose new method which includes the chest X-ray image segmentation pipeline with the multistep conditioned post-processing. As the result, we demonstrate significant improvement compare to any strong ”baseline” by reduction of the pneumothorax collapse regions which are missed out and of false positive detections. Our results indicate very high accuracy and strong robustness of the algorithm confirmed by corresponding efficiency on the two stage test dataset with a priori unknown and absolutely different distribution. Final Dice scores 0.8821 and 0.8614 for ”stage 1” and ”stage 2” test sets respectively were resulted in top 0.01% standing of the private leaderboard on Kaggle competition platform.
Deep Metric Learning Network using Proxies for Chromosome Classification in Karyotyping Test
Authors: Hwejin Jung, Bogyu Park, Seungwoo Hyun, Hanwoong Kim, Jinah Lee, Junseok Seo, Sunyoung Koo, Mina Lee
Date: 2020-01-29 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Retrieval, Metric Learning, Retrieval

In karyotyping, the classification of chromosomes is a tedious, complicated, and time-consuming process. It requires extremely careful analysis of chromosomes by well-trained cytogeneticists. To assist cytogeneticists in karyotyping, we introduce Proxy-ResNeXt-CBAM which is a metric learning based network using proxies with a convolutional block attention module (CBAM) designed for chromosome classification. RexNeXt-50 is used as a backbone network. To apply metric learning, the fully connected linear layer of the backbone network (ResNeXt-50) is removed and is replaced with CBAM. The similarity between embeddings, which are the outputs of the metric learning network, and proxies are measured for network training.
Proxy-ResNeXt-CBAM is validated on a public chromosome image dataset, and it achieves an accuracy of 95.86%, a precision of 95.87%, a recall of 95.9%, and an F-1 score of 95.79%. Proxy-ResNeXt-CBAM which is the metric learning network using proxies outperforms the baseline networks. In addition, the results of our embedding analysis demonstrate the effectiveness of using proxies in metric learning for optimizing deep convolutional neural networks. As the embedding analysis results show, Proxy-ResNeXt-CBAM obtains a 94.78% Recall@1 in image retrieval, and the embeddings of each chromosome are well clustered according to their similarity.
Single-Stage vs. Multi-Stage Machine Learning Algorithms for Prostate Segmentation in Magnetic Resonance Images
Authors: Anurag Garikipati, Rajesh Venkataraman
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation

Fusion of magnetic resonance images (MRI) with ultrasound has led to major improvements in precision diagnostics for prostate cancer. A key step in the fusion process is segmentation of the prostate in MRI and machine learning (ML) has proven to be a valuable tool for segmentation. In this paper, we compare two ML workflows for prostate segmentation; a single-stage and multi-stage ML algorithm to address the challenges of prostate segmentation.
The Performance of Deep U-Net Pre-Clinical Organ-wise Segmentation in the Presence of Low Counting Statistics.
Authors: R L Smith, Noah Evans, Victor Florence, S Paisey, C Marshall
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Micro-PET-CT allows non-invasive monitoring of biological processes, disease progression and therapy response. Morphological information provided by the CT allows organ / tissue delineation for subsequent quantification of the physiological information depicted by the PET. Deep learning with convolutional neural networks (CNNs) has achieved state-of-the-art performance for automated medical image segmentation and utilized successfully by our group in Micro-PET-CT (figure 1). The robustness of such approaches in the presence of noise addition / dose reduction of the CT data has not been explored. We thus simulate dose reduction of pre-clinical CT images using a Poisson noise model and evaluate the effect of segmentation performance with increasingly lower dose for 7 regions (skeleton, kidney, bladder, brain, lung, muscle and fat). data in the preclinical model with increase dose reduction. It can be observed that the accuracy of the segmentation measured by the DICE coefficient falls off as we simulate the reduction of CT dose. A 50% dose reduction was observed for all 5 test subjects to result in a mean (across all 7 organs) percentage reduction in DICE <25%. Adequate performance however is still observed in the DICE coefficient with a dose reduction of 30%, only an average of ~10% reduction in DICE is observed. This may have implications for utilising reduced dose CT coupled with a deep CNN for segmentation if the CT component is used to anatomically locate physiology on PET data.
Spherical function regularization for parallel MRI reconstruction
Authors: Yonggui Zhu
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: MRI Reconstruction

From the optimization point of view, a difficulty with parallel MRI with simultaneous coil sensitivity estimation is the multiplicative nature of the non-linear forward operator: the image being reconstructed and the coil sensitivities compete against each other, causing the optimization process to be very sensitive to small perturbations.
    This can, to some extent, be avoided by regularizing the unknown in a suitably ``orthogonal'' fashion. In this paper, we introduce such a regularization based on spherical function bases. To perform this regularization, we represent efficient recurrence formulas for spherical Bessel functions and associated Legendre functions. Numerically, we study the solution of the model with non-linear ADMM. We perform various numerical simulations to demonstrate the efficacy of the proposed model in parallel MRI reconstruction.
Motion Recovery from Radon Transformed Image Using Neural Networks
Authors: Haoran Chang, Debasis Mitra
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Decoder, Image Reconstruction

In this paper we address motion correction in image reconstruction. Patient motion, including breathing, is a persistent problem in medical imaging. Cardiac motion is particularly enigmatic and often ignored, except in high-speed imaging modalities like MRI. Motions may create artifacts to the extent that the image may have to be discarded. Since the beginning of medical imaging, motion correction remained an important subcategory of research. Motion corrections may be applied during or after tomographic image reconstruction. In this work we considered motion as a Gaussian blur at the image level. Discrete radon transform is applied to the blurred images to create corresponding noisy sinograms that mimic real imaging scenario. Our deep learning based tool recovered accurately (1) the blurring functions with an artificial convolutional neural network (CNN) directly from the sinograms, and (2) successfully reconstructed (inverse radon transformed) the noise-free images utilizing an adaptation of the convolutional encoder-decoder network (CED) from the literature. Our work shows that neural networks are not only capable of eliminating systematic noise in reconstruction but can also recover the noise model.
Targeted Dropout
Authors: Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
Date: 2018-10-20 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018

Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant. This makes it possible to compress neural networks without having a drastic effect on performance. We introduce targeted dropout, a strategy for post hoc pruning of neural network weights and units that builds the pruning mechanism directly into learning. At each weight update, targeted dropout selects a candidate set for pruning using a simple selection criterion, and then stochastically prunes the network via dropout applied to this set. The resulting network learns to be explicitly robust to pruning, comparing favourably to more complicated regularization schemes while at the same time being extremely simple to implement, and easy to tune.
PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks
Authors: Jiaxiang Wu, Yao Zhang, Haoli Bai, Huasong Zhong, Jinlong Hou, Wei Liu, Wenbing Huang, Junzhou Huang
Date: 2018-10-20 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Model Compression

Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices. Numerous model compression algorithms have been proposed, however, it is often difficult and time-consuming to choose proper hyper-parameters to obtain an efficient compressed model. In this paper, we propose an automated framework for model compression and acceleration, namely PocketFlow. This is an easy-to-use toolkit that integrates a series of model compression algorithms and embeds a hyper-parameter optimization module to automatically search for the optimal combination of hyper-parameters. Furthermore, the compressed model can be converted into the TensorFlow Lite format and easily deployed on mobile devices to speed-up the inference. PocketFlow is now open-source and publicly available at https://github.com/Tencent/PocketFlow.
Pruning neural networks: is it time to nip it in the bud?
Authors: Elliot J. Crowley, Jack Turner, Amos Storkey, Michael O'Boyle
Date: 2018-10-10 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018

Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed. However, the value of pruning has largely evaded scrutiny. In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations. First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network. Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable. Such architectures are powerful when trained from scratch. Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.