Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis

The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis paradigm for computational pathology. In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images. We propose deep Multi-Resolution Dictionary Learning (deepMRDL) in order to benefit from deep texture descriptors at multiple different spatial resolutions. We show the efficacy of the proposed approach through extensive experiments on four benchmark histology image datasets from different organs (colorectal cancer, breast cancer and breast lymphnodes) and tasks (namely, cancer grading, tissue phenotyping, tumor detection and tissue type classification). We also show that the proposed framework can employ most off-the-shelf CNNs models to generate effective deep texture descriptors.

Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles

We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture with evidence that ARCH representation transfers to a variety of pathology sub-tasks better than ImageNet features or representations obtained via self-supervised or multi-task learning on pathology images alone. We release our best model and invite other researchers to test it on their CP tasks.

Classification of COVID-19 via Homology of CT-SCAN

In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic. There are more than 110 Million infected cases on the globe, sofar. Due to its promptness and effective results computed tomography (CT)-scan image is preferred to the reverse-transcription polymerase chain reaction (RT-PCR). Early detection and isolation of the patient is the only possible way of controlling the spread of the disease. Automated analysis of CT-Scans can provide enormous support in this process. In this article, We propose a novel approach to detect SARS-CoV-2 using CT-scan images. Our method is based on a very intuitive and natural idea of analyzing shapes, an attempt to mimic a professional medic. We mainly trace SARS-CoV-2 features by quantifying their topological properties. We primarily use a tool called persistent homology, from Topological Data Analysis (TDA), to compute these topological properties. We train and test our model on the "SARS-CoV-2 CT-scan dataset" \citep{soares2020sars}, an open-source dataset, containing 2,481 CT-scans of normal and COVID-19 patients. Our model yielded an overall benchmark F1 score of $99.42\% $, accuracy $99.416\%$, precision $99.41\%$, and recall $99.42\%$. The TDA techniques have great potential that can be utilized for efficient and prompt detection of COVID-19. The immense potential of TDA may be exploited in clinics for rapid and safe detection of COVID-19 globally, in particular in the low and middle-income countries where RT-PCR labs and/or kits are in a serious crisis.

Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations

While high-resolution pathology images lend themselves well to `data hungry' deep learning algorithms, obtaining exhaustive annotations on these images is a major challenge. In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. The proposed approach, which we term as Self-Path, is a multi-task learning approach where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input data. We introduce novel domain specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the domain-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for classification of histology image patches when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available.

SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images

Synthetic images can be used for the development and evaluation of deep learning algorithms in the context of limited availability of annotations. In the field of computational pathology where histology images are large and visual context is crucial, synthesis of large tissue images via generative modeling is a challenging task due to memory and computing constraints hindering the generation of large images. To address this challenge, we propose a novel framework named as SAFRON to construct realistic large tissue image tiles from ground truth annotations while preserving morphological features and with minimal boundary artifacts at the seams. To this end, we train the proposed SAFRON framework based on conditional generative adversarial networks on large tissue image tiles from the Colorectal Adenocarcinoma Gland (CRAG) and DigestPath datasets. We demonstrate that our model can generate high quality and realistic image tiles of arbitrary large size after training it on relatively small image patches. We also show that training on synthetic data generated by SAFRON can significantly boost the performance of a standard algorithm for gland segmentation of colorectal cancer tissue images. Sample high resolution images generated using SAFRON are available at the URL:https://warwick.ac.uk/TIALab/SAFRON

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images

Object Segmentation is an important step in the work-flow of computational pathology. Deep learning based models as the best forming models require huge amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive, because it necessarily involves expert knowledge. This is perhaps best illustrated by medical tasks where measurements call for expensive machinery and labels are the fruit of a time-consuming analysis that draws from multiple human experts. As nuclei, cells and glands are fundamental objects for downstream analysis in histology, in this paper we propose a simple CNN-based approach to speed up collecting segmentation annotation for these objects by utilizing minimum input from an annotator. We show for nuclei and cells as small objects, one click inside objects is enough to have precise annotation. For glands as large objects, providing a squiggle to show the extend of gland can guide the model to outline the exact boundaries. This supervisory signals are fed to network as an auxiliary channels along with RGB channels. With detailed experiments, we show that our approach is generalizable, robust against variations in the user input and that it can be used to obtain annotations for completely different domains. Practically, a model trained on the masks generated by NuClick could achieve first rank in LYON19 challenge. Furthermore, as the output of our framework, we release two data-sets: 1) a dataset of lymphocyte annotations within IHC images and 2) a dataset of WBCs annotated in blood sample images.

Multi-Task Learning in Histo-pathology for Widely Generalizable Model

In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification.

PanNuke Dataset Extension, Insights and Baselines

The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides. However, it is imperative for the DL algorithms relying on nuclei-level details to be able to cope with data from `the clinical wild', which tends to be quite challenging. We study, and extend recently released PanNuke dataset consisting of ~200,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs. Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei and just over 24,000 labeled nuclei with segmentation masks. PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to the clinical wild and with minimal selection bias. We study the performance of segmentation and classification models when applied to the proposed dataset and demonstrate the application of models trained on PanNuke to whole-slide images. We provide comprehensive statistics about the dataset and outline recommendations and research directions to address the limitations of existing DL tools when applied to real-world CPath applications.