pwc-archive/papers-with-abstracts huggingface dataset:
Molecular Subtype Prediction for Breast Cancer Using H&E Specialized Backbone
Authors: Samaneh Abbasi Sureshjani, Anil Yuce, Simon Till Schönenberger, Maris Skujevskis, Uwe Schalles, Fabien Gaire, Konstanty Korski
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Prognosis, Transfer Learning, whole slide images

Identifying the molecular markers to categorize breast cancer is one of the key steps in determining the prognosis and treatment strategy. The standard clinical practice is to do this analysis based on multiple immunohistochemistry (IHC) stainings for each biomarker, which is expensive and inconsistent when lacking resources. In this work, we investigated the predictiveness of 
morphological characteristics of Hematoxylin and Eosin (H&E) stained tissues for molecular subtype analysis, as an initial step for direct treatment response prediction based on H&E whole slide images (WSI). Transfer learning using backbones pre-trained on natural images is a common practice to deal with the challenge of lack of large and precisely annotated datasets. However, using pre-training on natural images is not optimal for clinical images. To deal with this challenge and leverage large pools of unlabeled data, we propose to use a specialized backbone pre-trained on H&E WSI in a self-supervised setting, i.e. without any labels. Our experiments show that this backbone is capable of learning discriminating morphological characteristics from H&E images which are well predictive of the molecular subtypes in weakly supervised settings. Also, the network performs better in terms of generalization to unseen data from new scanner types, despite the relatively small size of the dataset used for pre-training the backbone.
Creating small but meaningful representations of digital pathology images
Authors: Corentin Guerendel, Phil Arnold, Benjamin Torben-Nielsen
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Image Retrieval, Representation Learning, whole slide images

Representation learning is a popular application of deep learning where an object (e.g.,
an image) is converted into a lower-dimensional representation that still encodes relevant
features of the original object. In digital pathology, however, this is more difficult because
whole slide images (WSIs) are tiled before processing because they are too large to process
at once. As a result, one WSI can be represented by thousands of representations - one for
each tile. Common strategies to aggregate the “tile-level representations” to a “slide-level
representation” rely on pooling operators or even attention networks, which all find some
weighted average of the tile-level representations.

In this work, we propose a novel approach to aggregate tile-level representations into
a single slide-level representation. Our method is based on clustering representations from
individual tiles that originate from a large pool of WSIs. Each cluster can be seen as encoding
a specific feature that might occur in a tile. Then, the final slide-level representation is a
function of the proportional cluster membership of all tiles from one WSI. We demonstrate
that we can represent WSIs in parsimonious representations and that these aggregated
slide-level representations allow for both WSI classification and, reversely, similar image
search.
Instance Segmentation of Multiple Myeloma Cells via Hybrid Task Cascade
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Cell Detection, Diagnostic, Instance Segmentation, Segmentation, Semantic Segmentation

Multiple Myeloma (MM) is a blood cancer that develops when plasma cells expand abnormally in the bone marrow. Early detection of MM is beneficial for accurate treatment in time and draws increasing recognition. There are several endeavors to construct computer-assisted automatic diagnostic tools for myeloma cell detection. Of late, deep learning based methods have been expected to apply for the detection and segmentation of cells of interest from microscopic images. In this paper, an ensemble framework is designed for the detection and segmentation of myeloma cells. Extensive experimental results on the SegPC-2021 Challenge dataset demonstrate that the proposed method achieves promising performance.
VTBIS: Vision Transformer for Biomedical Image Segmentation
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Decoder, Image Segmentation, MRI segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a novel network named Vision
Transformer for Biomedical Image Segmentation (VTBIS).
Our network splits the input feature maps into three parts
with 1 × 1, 3 × 3 and 5 × 5 convolutions in both encoder
and decoder. Concat operator is used to merge the features
before being fed to three consecutive transformer blocks
with attention mechanism embedded inside it. Skip con-
nections are used to connect encoder and decoder trans-
former blocks. Similarly, transformer blocks and multi scale
architecture is used in decoder before being linearly pro-
jected to produce the output segmentation map. We test
the performance of our network using Synapse multi-organ
segmentation dataset, Automated cardiac diagnosis chal-
lenge dataset, Brain tumour MRI segmentation dataset and
Spleen CT segmentation dataset. Without bells and whis-
tles, our network outperforms most of the previous state
of the art CNN and transformer based models using Dice
score and the Hausdorff distance as the evaluation met-
rics.
A Multi-scale Graph Network with Multi-head Attention for Histopathology Image Diagnosis
Authors: Xiaodan Xing, Yixin Ma, Lei Jin, Tianyang Sun, Zhong Xue, Feng Shi, Jinsong Wu, Dinggang Shen
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Diagnostic, Navigate

Hematoxylin-eosin (H&E) staining plays an essential role in brain glioma diagnosis, but reading pathologic images and generating diagnostic reports can be a tedious and laborious work. Pathologists need to combine and navigate extremely large images with different scales and to quantify different aspects for subtyping. In this work, we propose an automatic diagnosis algorithm to identify cell types and severity of H&E slides, in order to classify five major subtypes of glioma from whole slide pathological images. The proposed method is featured by a pyramid graph structure and an attention-based multi-instance learning strategy. We claim that our method not only improve the classification accuracy by utilizing multi-scale information, but also help to identify high risk patches. We summarized patches from multiple resolutions into a graph structure. The nodes of the pyramid graph are feature vectors extracted from image patches, and these vectors are connected by their spatial adjacency. We then fed the graph into the proposed model with self-attention and graph convolutions. Here, we used a multi-head self-attention architecture, where same self-attention blocks are stacked in parallel. As proven in Transformer networks, multiple attention maps herein capture comprehensive activation patterns from different subspace representation. Using the proposed method, the results show a 71% accuracy for glioma subtyping. The multiresolution attention maps generated from the proposed method could help locate proliferations and necrosis in the whole pathologic slide.
Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior
Authors: Da Ma, Donghuan Lu, Morgan Heisler, Setareh Dabiri, Sieun Lee, Gavin Weiguan Ding, Marinko V. Sarunic, Mirza Faisal Beg
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is widely used for the diagnosis of ophthalmic diseases with retinal alteration such as layer deformation and fluid accumulation. In this paper, a novel framework was proposed to segment retinal layers with fluid presence. The main contribution of this study is two folds: 1) we developed a cascaded network framework to incorporate the prior structural knowledge; 2) we proposed a novel two-path deep neural network which includes both the U-Net architecture as well as the original implementation of the fully convolutional network, concatenated into a final multi-level dilated layer to achieve accurate simultaneous layer and fluid segmentation. Cross validation experiments proved that the proposed network has superior performance comparing with the state-of-the-art methods by up to 3%, and incorporating the relative positional map structural prior information could further improve the performance (up to 1%) regardless of the network.
Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Segmentation
Authors: Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Reconstruction, object-detection, Object Detection, Translation

Calcified plaque in the aorta and pelvic arteries is associated with coronary artery calcification and is a strong predictor of heart attack. Current calcified plaque detection models show poor generalizeability to different domains (ie. pre-contrast vs. post-contrast CT scans). Many recent works have shown how cross domain object detection can be improved using an image translation model which translates between domains using a single shared latent space. However, while current image translation models do a good job preserving global/intermediate level structures they often have trouble preserving tiny structures. In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis. Recent works on image reconstruction show that complex real-world images are better reconstructed using a union of subspaces approach. Since small image patches are used to train the image translation model, it makes sense to enforce that each patch be represented by a linear combination of subspaces which may correspond to the different parts of the body present in that patch. Motivated by this, we propose an image translation network using a shared union of subspaces constraint and show our approach preserves subtle structures (plaques) better than the conventional method. We further applied our method to a cross domain plaque detection task and show significant improvement compared to the state-of-the art method.
Laplacian pyramid-based complex neural network learning for fast MR imaging
Authors: Haoyun Liang, Yu Gong, Hoel Kervadec, Jing Yuan, Hairong Zheng, Shanshan Wang
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data. Specifically, three major contributions have been made: 1) A new framework has been proposed to explore the encouraging multi-scale properties of Laplacian pyramid decomposition; 2) A cascaded multi-scale network architecture with complex convolutions has been designed under the proposed framework; 3) Experimental validations on an open source dataset fastMRI demonstrate the encouraging properties of the proposed method in preserving image edges and fine textures.
Automatic segmentation of stroke lesions in non-contrast computed tomography with convolutional neural networks
Authors: Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Lesion Segmentation

Manual lesion segmentation for non-contrast computed tomography (NCCT), a common modality for volumetric follow-up assessment of ischemic strokes, is time-consuming and subject to high inter-observer variability. Our approach uses a combination of a 3D convolutional neural network (CNN) combined with post-processing methods. A total of 291 multi-center clinical NCCT datasets were used: 204 for CNN training, 48 for validation and developing post-processing methods, and 39 for testing. The testing datasets were from centers that did not contribute to the training and validation sets, and were segmented by two or three neuroradiologists. We achieved a mean Dice score of 0.42 on the out-of-distribution test set, which was significantly improved to 0.45 with post-processing. The automatically segmented lesion volumes were not significantly different from the lesion volumes determined by manual segmentations from multiple  observers. As the model was trained on datasets from multiple centers, it is broadly applicable and publicly available from http://dx.doi.org/10.21227/jps9-0b57 .
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks
Authors: Richard Du, Varut Vardhanabhuti
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Liver Segmentation, Transfer Learning

Training deep convolution neural network requires a large amount of data to obtain good performance and generalisable results. Transfer learning approaches from datasets such as ImageNet had become important in increasing accuracy and lowering training samples required. However, as of now, there has not been a popular dataset for training 3D volumetric medical images. This is mainly due to the time and expert knowledge required to accurately annotate medical images. In this study, we present a method in extracting labels from DICOM metadata that information on the appearance of the scans to train a medical domain 3D convolution neural network. The labels include imaging modalities and sequences, patient orientation and view, presence of contrast agent, scan target and coverage, and slice spacing. We applied our method and extracted labels from a large amount of cancer imaging dataset from TCIA to train a medical domain 3D deep convolution neural network. We evaluated the effectiveness of using our proposed network in transfer learning a liver segmentation task and found that our network achieved superior segmentation performance (DICE=90.0%) compared to training from scratch (DICE=41.8%). Our proposed network shows promising results to be used as a backbone network for transfer learning to another task. Our approach along with the utilising our network, can potentially be used to extract features from large-scale unlabelled DICOM datasets.
Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification
Authors: Devanshu Arya, Richard Olij, Deepak K. Gupta, Ahmed El Gazzar, Guido van Wingen, Marcel Worring, Rajat Mani Thomas
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Disease Prediction

Geometric deep learning methods such as graph convolutional networks have recently proven to deliver generalized solutions in disease prediction using medical imaging. In this paper, we focus particularly on their use in autism classification. Most of the recent methods use graphs to leverage phenotypic information about subjects (patients or healthy controls) as additional contextual information. To do so, metadata such as age, gender and acquisition sites are utilized to define intricate relations (edges) between the subjects. We alleviate the use of such non-imaging metadata and propose a fully imaging-based approach where information from structural and functional Magnetic Resonance Imaging (MRI) data are fused to construct the edges and nodes of the graph. To characterize each subject, we employ brain summaries. These are 3D images obtained from the 4D spatiotemporal resting-state fMRI data through summarization of the temporal activity of each voxel using neuroscientifically informed temporal measures such as amplitude low frequency fluctuations and entropy. Further, to extract features from these 3D brain summaries, we propose a 3D CNN model. We perform analysis on the open dataset for autism research (full ABIDE I-II) and show that by using simple brain summary measures and incorporating sMRI information, there is a noticeable increase in the generalizability and performance values of the framework as compared to state-of-the-art graph-based models.
Addressing The False Negative Problem of MRI Reconstruction Networks by Adversarial Attacks and Robust Training
Authors: Kaiyang Cheng, Francesco Calivá, Rutwik Shah, Misung Han, Sharmila Majumdar, Valentina Pedoia
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Diagnostic, MRI Reconstruction, SSIM

Deep learning models have been shown to be successful in accelerating MRI reconstruction, over traditional methods. However, it has been observed that these methods tend to miss rare small features, such as meniscal tears, subchondral osteophyte, etc. in musculoskeletal applications. This is a concerning finding as these small and rare features are the particularly relevant in clinical diagnostic settings. Additionally, such potentially dangerous loss of details in the reconstructed images are not reflected by global image fidelity metrics such as mean-square error (MSE) and structural similarity metric (SSIM). In this work, we propose a framework to find the worst-case false negatives by adversarially attacking the trained models and improve the models' ability to reconstruct the small features by robust training.
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
Authors: Jun Ma, Zhan Wei, Yiwen Zhang, Yixin Wang, Rongfei Lv, Cheng Zhu, Gaoxiang Chen, Jianan Liu, Chao Peng, Lei Wang, Yunpeng Wang, Jianan Chen
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation

Incorporating distance transform maps of ground truth into segmentation CNNs has been an interesting new trend in the last year. Despite many great works leading to improvements in a variety of segmentation tasks, the comparison among these methods has not been well studied.
In this paper, our \emph{first contribution} is to summarize the latest developments of these methods in the 3D medical segmentation field.
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
These experiments highlight that all the five benchmark methods can bring performance gains to baseline V-Net. However, the implementation details have a noticeable impact on the performance, and not all the methods hold the benefits on different datasets.
Finally, we suggest the best practices and indicate unsolved problems for incorporating distance transform maps into CNNs, which we hope would be useful for the community. The codes and trained models are publicly available at \url{https://github.com/JunMa11/SegWithDistMap}.
Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation
Authors: Francesco Caliva', Rutwik Shah, Upasana Upadhyay Bharadwaj, Sharmila Majumdar, Peder Larson, Valentina Pedoia
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: MRI Reconstruction, Segmentation

Magnetic Resonance Image (MRI) acquisition, reconstruction and tissue segmentation are usually considered separate problems. This can be limiting when it comes to rapidly extracting relevant clinical parameters. In many applications, availability of reconstructed images with high fidelity may not be a priority as long as biomarker extraction is reliable and feasible. Built upon this concept, we demonstrate that it is possible to perform tissue segmentation directly from highly undersampled k-space and obtain quality results comparable to those in fully-sampled scenarios. We propose 'TB-recon', a 3D task-based reconstruction framework. TB-recon simultaneously reconstructs MRIs from raw data and segments tissues of interest. To do so, we devised a network architecture with a shared encoding path and two task-related decoders where features flow among tasks. We deployed TB-recon on a set of (up to $24\times$) retrospectively undersampled MRIs from the Osteoarthritis Initiative dataset, where we automatically segmented knee cartilage and menisci. An experimental study was conducted showing the superior performance of the proposed method over a combination of a standard MRI reconstruction and segmentation method, as well as alternative deep learning based solutions. In addition, our ablation study highlighted the importance of skip connections among the decoders for the segmentation task. Ultimately, we conducted a reader study, where two musculoskeletal radiologists assessed the proposed model’s reconstruction performance.
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction
Authors: William Le, Francisco Perdigón Romero, Samuel Kadoury
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: image-classification, Image Classification, Medical Image Classification, Survival Prediction

Medical image classification performance worsens in multi-domain datasets, caused by radiological image differences across institutions, scanner manufacturer, model and operator. Deep learning is well-suited for learning image features with priors encoded as constraints during the training process.  In this work, we apply a ResNeXt classification network augmented with an FCN preprocessor subnetwork to a public TCIA head and neck cancer dataset. The training goal is survival prediction of radiotherapy cases based on pre-treatment FDG-PET/CT scans, acquired across 4 different hospitals.  We show that the preprocessor sub-network acts as a embedding normalizer and improves over state-of-the-art results of 70% AUC to 76%.
Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations
Authors: Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Bir Bhanu
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Disentanglement, Generative Adversarial Network, Prognosis

Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma). Overall prognostic assessment requires analysis of multiple modalities: imaging, molecular and clinical. To optimize this assessment pipeline, this paper develops the first deep learning-based system that uses MRI data to predict 19/20 co-gain, a mutation that indicates median survival. It addresses two key challenges when dealing with deep learning algorithms and medical data: lack of data and high data imbalance. To tackle these challenges, we propose a unified approach that consists of a Feature Disentanglement based Generative Adversarial Network (FeaD-GAN) for generating synthetic images. FeaD-GAN projects disentangled features into a high dimensional space and re-samples them from a pseudo-large data distribution to generate synthetic images from very limited data. A thorough analysis is performed to (a) characterize aspects of visual manifestation of 19/20 co-gain to demonstrate the effectiveness of FeaD-GAN and (b) demonstrate that not only do the imaging biomarkers of 19/20 co-gain exist, but also that they are reproducible.