Models, code, and papers for "Shan E Ahmed Raza":

XY Network for Nuclear Segmentation in Multi-Tissue Histology Images

Dec 16, 2018
Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Jin Tae Kwak, Nasir Rajpoot

Nuclear segmentation within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow, due to the ability for nuclear features to act as key diagnostic markers. The development of automated methods for nuclear segmentation enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for automated nuclear segmentation that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. We demonstrate state-of-the-art performance compared to other methods on four independent multi-tissue histology image datasets. Furthermore, we propose an interpretable and reliable evaluation framework that effectively quantifies nuclear segmentation performance and overcomes the limitations of existing performance measures.


  Click for Model/Code and Paper
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images

Jun 28, 2018
Priya Lakshmi Narayanan, Shan E Ahmed Raza, Andrew Dodson, Barry Gusterson, Mitchell Dowsett, Yinyin Yuan

Ki67 is an important biomarker for breast cancer. Classification of positive and negative Ki67 cells in histology slides is a common approach to determine cancer proliferation status. However, there is a lack of generalizable and accurate methods to automate Ki67 scoring in large-scale patient cohorts. In this work, we have employed a novel deep learning technique based on hypercolumn descriptors for cell classification in Ki67 images. Specifically, we developed the Simultaneous Detection and Cell Segmentation (DeepSDCS) network to perform cell segmentation and detection. VGG16 network was used for the training and fine tuning to training data. We extracted the hypercolumn descriptors of each cell to form the vector of activation from specific layers to capture features at different granularity. Features from these layers that correspond to the same pixel were propagated using a stochastic gradient descent optimizer to yield the detection of the nuclei and the final cell segmentations. Subsequently, seeds generated from cell segmentation were propagated to a spatially constrained convolutional neural network for the classification of the cells into stromal, lymphocyte, Ki67-positive cancer cell, and Ki67-negative cancer cell. We validated its accuracy in the context of a large-scale clinical trial of oestrogen-receptor-positive breast cancer. We achieved 99.06% and 89.59% accuracy on two separate test sets of Ki67 stained breast cancer dataset comprising biopsy and whole-slide images.

* MIDL 2018 Conference 

  Click for Model/Code and Paper
Deconvolving convolution neural network for cell detection

Jun 18, 2018
Shan E Ahmed Raza, Khalid AbdulJabbar, Mariam Jamal-Hanjani, Selvaraju Veeriah, John Le Quesne, Charles Swanton, Yinyin Yuan

Automatic cell detection in histology images is a challenging task due to varying size, shape and features of cells and stain variations across a large cohort. Conventional deep learning methods regress the probability of each pixel belonging to the centre of a cell followed by detection of local maxima. We present deconvolution as an alternate approach to local maxima detection. The ground truth points are convolved with a mapping filter to generate artifical labels. A convolutional neural network (CNN) is modified to convolve it's output with the same mapping filter and is trained for the mapped labels. Output of the trained CNN is then deconvolved to generate points as cell detection. We compare our method with state-of-the-art deep learning approaches where the results show that the proposed approach detects cells with comparatively high precision and F1-score.


  Click for Model/Code and Paper
Micro-Net: A unified model for segmentation of various objects in microscopy images

Apr 22, 2018
Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban, Simon Graham, David Epstein, Stella Pelengaris, Michael Khan, Nasir M. Rajpoot

Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of its parameters. It trains itself at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms the state-of-the-art.


  Click for Model/Code and Paper
Machine Learning at Microsoft with ML .NET

May 15, 2019
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Eric Erhardt, Costin Eseanu, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Shon Katzenberger, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Sarthak Shah, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned.


  Click for Model/Code and Paper