pwc-archive/papers-with-abstracts huggingface dataset:
An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net
Authors: Qing Xu, Wenting Duan
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Cell Segmentation, Image Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

Nuclei segmentation is an important step in the task of medical image analysis. Nowadays,
deep learning techniques based on Convolutional Neural Networks (CNNs) have become
prevalent methods in nuclei segmentation. In this paper, we propose a network called
Multi-scale Split-Attention U-Net (MSAU-Net) for further improving the performance of
cell segmentation. MSAU-Net is based on U-Net architecture and the original blocks used to
down-sampling and up-sampling paths are replaced with Multi-scale Split-Attention blocks
for capturing independent semantic information of nuclei images. A public microscopy image
dataset from 2018 Data Science Bowl grand challenge is selected to train and evaluate
MSAU-Net. By running trained models on the test set, our model reaches average Intersection
over Union (IoU) of 0.851, which is better than other prominent models, especially
4.8 percent higher than the original U-Net. For other evaluation metrics including accuracy,
precision, recall and F1-score, MSAU-Net shows better performance in the most of
indicators. The outstanding result reveals that our proposed model presents a promising
nuclei segmentation method for the microscopy image analysis.
Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images
Authors: Hanyun Zhang, Tami Grunewald, Ayse U. Akarca, Teresa Marafioti, Jonathan A. Ledermann, Yinyin Yuan
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Cell Detection, Classification

Deep-learning-based automatic analysis of the multiplex immunohistochemistry (mIHC) enables distinct cell populations to be localized on a large scale, providing insights into disease biology and therapeutic targets. However, standard deep-learning pipelines performed cell detection and classification as two-stage tasks, which is computationally inefficient and faces challenges to incorporate neighbouring tissue context for determining the cell identity. To overcome these limitations and to obtain a more accurate mapping of cell phenotypes, we presented a symmetric dense inception neural network for detecting and classifying cells in mIHC slides simultaneously. The model was applied with a novel stop-gradient strategy and a loss function accounted for class imbalance. When evaluated on an ovarian cancer dataset containing 6 cell types, the model achieved an F1 score of 0.835 in cell detection, and a weighted F1-score of 0.867 in cell classification, which outperformed separate models trained on individual tasks by 1.9% and 3.8% respectively. Taken together, the proposed method boosts the learning efficiency and prediction accuracy of cell detection and classification by jointly learning from both tasks.
Robust quad-tree based registration of whole slide images
Authors: Christian Marzahl, Frauke Wilm, Christine Kröger, Franz F Dressler, Lars Tharun, Sven Perner, Christof Bertram, Jörn Voigt, Robert Klopfleisch, Andreas Maier, Marc Aubreville, Katharina Breininger
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Diagnostic, whole slide images

The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the first step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, fluctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a specific image modality, such as histology or cytology, or rely on a specific preparation protocol. To minimise these effects, we developed a robust WSI registration, which differs from previous methods by the following new aspect: We introduce a multi-scale approach based on a quad-tree (QT), with several termination criteria that makes the algorithm particularly insensitive to tissue artefacts and that further allows to estimate a piece-wise affine transformation. We validated our method on five scanner systems and 60 WSIs with different stainings. Our results outperformed any publicly available WSI registration method. The QT code, WSI landmarks and tools used to create the validation dataset are made publicly available.
Local Regularisation in Histological Registration forPreserving Cell Morphology
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9

Histopathological diagnoses may depend on both the presence/absence of specific biomark-ers, as well as cell morphology.  When registering histology images, due to structural dif-ferences in consecutive slices or damage during staining, affine transforms may fail to alignhigh-level structures.  As such, non-rigid registration can be used to align such structures.This can distort cell morphology, which may be problematic when a diagnosis or inferencedepends  on  such.   We  introduce  a  regularisation  approach  in  which  a  cell  segmentationinduces acellularityimage, indicating a regions degree of cellularity.  This is used to weighta pixel-wise regularity term which encourages such regions to be rigid,  while permittingnon-rigid deformations elsewhere in the image.  We show that under certain configurationsthis method results in no loss of accuracy (as measured by annotated keypoint distances),with potential improvements to measured levels of non-rigidity in cell regions.
Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9

Generating annotated pairs of realistic tissue images along with their annotations is a challenging task in computational histopathology. Such synthetic images and their annotations can be useful in training and evaluation of algorithms in the domain of computational pathology. To address this, we present an interactive framework to generate pairs of realistic colorectal cancer histology images with corresponding tissue component masks from the input gland layout. The framework shows the ability to generate realistic qualitative tissue images preserving morphological characteristics including stroma, goblet cells and glandular lumen. We show the appearance of glands can be controlled by user inputs such as number of glands, their locations and sizes. We also validate the quality of generated annotated pair with help of the gland segmentation algorithm.
Graph Based Neural Networks for Interrogating Multiplexed Tissue Samples
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9

Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell-microenvironment interactions. Noise, imaging artifacts, and the variation in protein expression make this a particularly challenging problem. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to identify communities of cells related to tumour stage. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets.
Weakly Supervised Global-Local Feature Learning for Cervical Cytology Image Analysis
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Pseudo Label

Existing supervised Convolutional Neural Network (CNN) approaches for cervical cytology image analysis generally rely on the heavy manual annotation for each cell or cell mass and thus lead to extensive time and effort. In this paper, we propose a weakly supervised global-local learning for cervical cytology image analysis. It aims to perform the classification for region of interests (ROIs) and further classify the cells only with the ROI labels. Specifically, the proposed method firstly detects the cells within ROI and extracts the CNN features of cells. Then attention-based bidirectional LSTM (Att-BLSTM) is applied to explore the global contextual information of ROI. On the other hand, the Vision Transformer (ViT) is used to exploit the local attentive representations of the cells in ROIs. The cross attention (CA) is applied to incorporate the global contextual features and local patterns and thus generates more discriminative feature representation of ROI. More importantly, the CA score is used as the pseudo label to select top and least attentive cells. Therefore, the in-the-class and out-of-the-class CA branches are trained to achieve the cell classification. Experimental results demonstrate the effectiveness of our method for cervical cytology ROI and cell classification, and the weak supervision of the image-level label has great potential to promote the automatic whole slide cervical image analysis and alleviate the workload of cytologists.
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data
Authors: Amaury Leroy, Kumar Shreshtha, Marvin Lerousseau, Théophraste Henry, Théo Estienne, Marion Classe, Nikos Paragios, Vincent Grégoire, Eric Deutsch
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Prognosis

The pathological analysis of biopsy specimens is essential to cancer diagnosis, treatment selection and prognosis. However, biopsies are only taken from part of the tumor and cannot assess the full cellular extension. Such information is essential to delineate as accurately as possible the tumor volume on a three-dimensional basis. Furthermore, they require highly qualified personnel and are associated with significant risks. The aim of our work is to provide alternative means to gather clinical information related to histology through MR image translation towards virtual pathological content generation. Conventional approaches to address this objective exploit paired data that is cumbersome to achieve due to tissue collapse and deformation, different resolution scales and absence of plane correspondences. In this paper, we introduce a versatile, scalable and robust closed-loop dual synthesis concept that composes two generation mechanisms - cycle-consistent generative adversarial networks -, one exploring weakly paired data and a subsequent harnessing virtually generated paired correspondences. The clinical relevance and interest of our framework are demonstrated in prostate cancer patients. Qualitative clinical assessment and quantitative reconstruction measurements demonstrate the potential of our approach.
A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs
Authors: Hammam Alghamdi, Navid Alemi Koohbanani, Nasir Rajpoot, Shan E Ahmed Raza
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Prognosis, whole slide images

Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer. However, handling large whole slide images (WSIs) is a vital challenge that these algorithms encounter. In this paper, we propose a novel technique that creates a compressed representation of histology images. This representation is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells. The compression technique is used to predict the status of ER and PR expressions from H&E images. Our results show that the proposed compression technique can improve the prediction performance by 11-26%.
Towards a gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. Through microscopic inspection, this assessment estimates the intensity and integrity of the membrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: a subjective decision that depends on the interpretation of the pathologist.
This paper presents the preliminary data analysis of the annotations of three pathologists over the same set of samples obtained using 20x magnification and including 1,252 non-overlapping biopsy patches. We evaluate the intra- and inter-expert variability achieving substantial and moderate agreement, respectively, according to Fleiss' Kappa coefficient, as a previous stage towards a generation of a HER2 breast cancer biopsy gold-standard using supervised learning from multiple pathologist annotations.
Automated Quanti cation Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images
Authors: Azam Hamidinekoo, Anna Kelsey, Nicholas Trahearn, Joanna Selfe, Janet Shipley, Yinyin Yuan
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: whole slide images

Tumour cells require resources to survive and proliferate. In order to be provided with a supportive micro-environment rich with resources to sustain optimal growth, tumour cells tend to reside in close proximity to a network of blood vessels. Quantification of blood microvessel density can be a useful measure to investigate the importance of resource limitation in tumours for prognostication and assigning treatment and mode of drug delivery. Currently, immunohistochemistry (IHC) with specic antibodies and the subsequent detection of its binding in the tumour tissue are used to identify microvessels. The automated quantification of blood microvessels in Hematoxylin and Eosin (H&E) stained images is not widely studied because microvessels are very complex and heterogeneous. In addition, their manual identification is tedious, time-consuming and subjective. We investigate whether the vasculature in H&E can be robustly identified in whole slide sections that would ultimately avoid the need for IHC and manual annotations. We propose an artificial intelligence model based on Generative Adversarial Networks (GAN) that, from an input H&E image, can generate a synthetic ERG stained image, highlighting vessel structures. We also trained a spatially constrained Convolutional Neural Network (CNN) to identify single cells on ERG stained whole slide images, and found good concordance between detected cells in synthetic and real ERG. This pipeline was evaluated on 2002 image patches of size 2000x2000 pixels, sampled from 9 whole slide images. We achieved the mean R-square of 0.70+-0.14 in our testing set. This pipeline can pave the way to study proximity of tumours cells to blood vessels. This approach has the potential to reduce the use of IHC and tissues and enable large quantitative studies.
Dual-stream CNN: Joint Edge and Object for Nucleus Segmentation
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Edge Detection, Object, Segmentation, Semantic Segmentation

 Nucleus segmentation is a significant task that can contribute greatly to re-ducing the time to develop and validate visual biomarkers for new digital pathology datasets. There are large numbers of clusters of crowed nuclei that mainly causes poor performance of deep learning method. This segmentation task normally can be divided into two sub-tasks: (1) edge detection, and (2) object segmentation. However, the proposed methods before have two stag-es: (1) do edge detection and object segmentation respectively, and (2) fuse the edge and object results from stage one. In this paper, we proposed a Dual-stream CNN, which joints edge and object in an end-to-end segmentation network and do feature fusion at extractor by using a cross-stitch module. We use two same U-nets for edge detection and object segmentation and design a cross-stitch module at each stage of both U-nets to combine edge and object features. We validate our method using the Multi-organ Nucleus Segmentation (MoNuSeg) Challenge. Our Dual-stream CNN achieves the state-of-the-art performance on this dataset.
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations
Authors: Niccolò Marini, Sebastian Otálora, Francesco Ciompi, Gianmaria Silvello, Stefano Marchesin, Simona Vatrano, Gianziana Buttafuoco, Manfredo Atzori, Henning Müller
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Multiple Instance Learning, whole slide images

Whole slide images (WSIs) are high-resolution digitized images of tissue samples, stored including different magnification levels.
WSIs datasets often include only global annotations, available thanks to pathology reports. 
Global annotations refer to global findings in the high-resolution image and do not include information about the location of the regions of interest or the magnification levels used to identify a finding.
This fact can limit the training of machine learning models, as WSIs are usually very large and each magnification level includes different information about the tissue.
This paper presents a Multi-Scale Task Multiple Instance Learning (MuSTMIL) method, allowing to better exploit data paired with global labels and to combine contextual and detailed information identified at several magnification levels.
The method is based on a multiple instance learning framework and on a multi-task network, that combines features from several magnification levels and produces multiple predictions (a global one and one for each magnification level involved).
MuSTMIL is evaluated on colon cancer images, on binary and multilabel classification.
MuSTMIL shows an improvement in performance in comparison to both single scale and another multi-scale multiple instance learning algorithm, demonstrating that MuSTMIL can help to better deal with global labels targeting full and multi-scale images.
SMILE: Sparse-Attention based Multiple Instance Contrastive Learning for Glioma Sub-type Classification Using Pathological Image
Authors: Mengkang Lu, Yongsheng Pan, Dong Nie, Feng Shi, Feihong Liu, Yong Xia, Dinggang Shen
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Contrastive Learning

Gliomas are the most prevalent malignant brain tumor in adults, and can be classified intofour  typical  sub-types  based  on histological  features.   Histological  diagnosis  by  pathologists via microscopic visual inspection of pathological slides has been the gold standard for glioma grading, especially hematoxylin and eosin (H&E) sections.  However, due to spatial heterogeneity and complex tumor micro-environment, it is difficult and time-consuming for pathologists to differentiate glioma sub-types.  In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification.   First,  we  use  contrastive  learning  to  extract  meaningful  representations  from pathological images.  Second, we propose the sparse-attention multi-instance learning aggregator to get sparse instance representations in a bag for label prediction.  We validate the proposed SMILE method using a glioma dataset from The Cancer Genome Atlas (TCGA).Experimental results show superior performance of our method over competing ones.  Ablation study further demonstrates the effectiveness of our design of SMILE.
Ensemble clustering for histopathological images segmentation using convolutional autoencoders
Authors: Anonymous
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Clustering

Unsupervised deep learning using autoencoders has shown excellent results in image analysis and computer vision.  However,  only few studies have been presented in the field of digital pathology, where proper labelling of the objects of interest is a particularly costly and difficult task.  Thus, having a first fully unsupervised segmentation could greatly help in the analysis process of such images.  In this paper, many architectures of convolutional autoencoders have been compared to study the influence of three main hyperparameters: (1) number of convolutional layers, (2) number of convolutions in each layer and (3) size of the latent space.  Different clustering algorithms are also compared and we propose a new way to obtain more precise results by applying ensemble clustering techniques which consists in combining multiple clustering results.
Detecting genetic alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts.
Authors: Johannes Höhne, Jacob de Zoete, Arndt A Schmitz, Tricia Bal, Emmanuelle di Tomaso, Matthias Lenga
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Multiple Instance Learning

In this paper, we describe the machine learning problem of identifying different types of tumors based on digital pathology images. Given a set of Hematoxylin and Eosin (H&E) stained images of thyroid tumors, we train deep learning models to detect two known molecular oncogenic drivers: BRAF mutations and NTRK gene fusions. We implement an attention-based multiple instance learning (MIL) classifier and we assess its generalization within and across three independent cohorts. We find that the model can detect both oncogenic drivers with the MIL approach, however the problem remains challenging: our exhaustive evaluation scenarios exemplify unknown data drifts and batch effects in digital pathology as the model performance decreases when processing images from an unseen cohort. These findings highlight the necessity of rich and diverse datasets for training and evaluation as well as methods for domain-agnostic learning.
Deep Learning for interpretable end-to-end survival prediction in gastrointestinal cancer histopathology
Authors: Narmin Ghaffari Laleh, Amelie Echle, Hannah Sophie Muti, Katherine Jane Hewitt, Volkmar Schulz, Jakob Nikolas Kather
Date: 2021-07-20 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: Survival Prediction, whole slide images

Digitized histopathology slides contain a wealth of information, only a fraction of which is being used in clinical routine. Deep learning can extract subtle visual features from digitized slides and thus can infer clinically relevant endpoints from raw image data. While classification and regression methods are well established in this domain, end-to-end prediction of patient survival still remains a comparably novel approach. To account for different follow-up times and censored data, previous approaches have largely used discretized survival data. Here, we demonstrate and validate EE-Surv, a powerful yet algorithmically simple method to predict survival directly from whole slide images which we validate in colorectal and gastric cancer, two clinically relevant and markedly different tumor types. We experimentally show that our method yields a highly significant prediction of survival and enables explainability of predictions. Our method is publicly available under an open-source license and can be applied to any type of disease.