9 research outputs found
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
In digital pathology, the spatial context of cells is important for cell
classification, cancer diagnosis and prognosis. To model such complex cell
context, however, is challenging. Cells form different mixtures, lineages,
clusters and holes. To model such structural patterns in a learnable fashion,
we introduce several mathematical tools from spatial statistics and topological
data analysis. We incorporate such structural descriptors into a deep
generative model as both conditional inputs and a differentiable loss. This
way, we are able to generate high quality multi-class cell layouts for the
first time. We show that the topology-rich cell layouts can be used for data
augmentation and improve the performance of downstream tasks such as cell
classification.Comment: To be published in proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 202
Exascale Deep Learning to Accelerate Cancer Research
Deep learning, through the use of neural networks, has demonstrated
remarkable ability to automate many routine tasks when presented with
sufficient data for training. The neural network architecture (e.g. number of
layers, types of layers, connections between layers, etc.) plays a critical
role in determining what, if anything, the neural network is able to learn from
the training data. The trend for neural network architectures, especially those
trained on ImageNet, has been to grow ever deeper and more complex. The result
has been ever increasing accuracy on benchmark datasets with the cost of
increased computational demands. In this paper we demonstrate that neural
network architectures can be automatically generated, tailored for a specific
application, with dual objectives: accuracy of prediction and speed of
prediction. Using MENNDL--an HPC-enabled software stack for neural architecture
search--we generate a neural network with comparable accuracy to
state-of-the-art networks on a cancer pathology dataset that is also
faster at inference. The speedup in inference is necessary because of the
volume and velocity of cancer pathology data; specifically, the previous
state-of-the-art networks are too slow for individual researchers without
access to HPC systems to keep pace with the rate of data generation. Our new
model enables researchers with modest computational resources to analyze newly
generated data faster than it is collected.Comment: Submitted to IEEE Big Dat
Keratin 17 Modulates the Immune Topography of Pancreatic Cancer
BACKGROUND: The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival.
METHODS: Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs.
RESULTS: K17 expression had profound effects on the exclusion of intratumoral CD8+ T cells and was also associated with decreased numbers of peritumoral CD8+ T cells, CD16+ macrophages, and CD163+ macrophages (p \u3c 0.0001). The differences in the intratumor and peritumoral CD8+ T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations.
CONCLUSIONS: Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer
Localization in the Crowd with Topological Constraints
We address the problem of crowd localization, i.e., the prediction of dots
corresponding to people in a crowded scene. Due to various challenges, a
localization method is prone to spatial semantic errors, i.e., predicting
multiple dots within a same person or collapsing multiple dots in a cluttered
region. We propose a topological approach targeting these semantic errors. We
introduce a topological constraint that teaches the model to reason about the
spatial arrangement of dots. To enforce this constraint, we define a
persistence loss based on the theory of persistent homology. The loss compares
the topographic landscape of the likelihood map and the topology of the ground
truth. Topological reasoning improves the quality of the localization algorithm
especially near cluttered regions. On multiple public benchmarks, our method
outperforms previous localization methods. Additionally, we demonstrate the
potential of our method in improving the performance in the crowd counting
task.Comment: AAAI 202
Evaluating histopathology transfer learning with ChampKit
Histopathology remains the gold standard for diagnosis of various cancers.
Recent advances in computer vision, specifically deep learning, have
facilitated the analysis of histopathology images for various tasks, including
immune cell detection and microsatellite instability classification. The
state-of-the-art for each task often employs base architectures that have been
pretrained for image classification on ImageNet. The standard approach to
develop classifiers in histopathology tends to focus narrowly on optimizing
models for a single task, not considering the aspects of modeling innovations
that improve generalization across tasks. Here we present ChampKit
(Comprehensive Histopathology Assessment of Model Predictions toolKit): an
extensible, fully reproducible benchmarking toolkit that consists of a broad
collection of patch-level image classification tasks across different cancers.
ChampKit enables a way to systematically document the performance impact of
proposed improvements in models and methodology. ChampKit source code and data
are freely accessible at https://github.com/kaczmarj/champkit .Comment: Submitted to NeurIPS 2022 Track on Datasets and Benchmarks. Source
code available at https://github.com/kaczmarj/champki
Recommended from our members
Keratin 17 modulates the immune topography of pancreatic cancer.
BACKGROUND: The immune microenvironment impacts tumor growth, invasion, metastasis, and patient survival and may provide opportunities for therapeutic intervention in pancreatic ductal adenocarcinoma (PDAC). Although never studied as a potential modulator of the immune response in most cancers, Keratin 17 (K17), a biomarker of the most aggressive (basal) molecular subtype of PDAC, is intimately involved in the histogenesis of the immune response in psoriasis, basal cell carcinoma, and cervical squamous cell carcinoma. Thus, we hypothesized that K17 expression could also impact the immune cell response in PDAC, and that uncovering this relationship could provide insight to guide the development of immunotherapeutic opportunities to extend patient survival. METHODS: Multiplex immunohistochemistry (mIHC) and automated image analysis based on novel computational imaging technology were used to decipher the abundance and spatial distribution of T cells, macrophages, and tumor cells, relative to K17 expression in 235 PDACs. RESULTS: K17 expression had profound effects on the exclusion of intratumoral CD8+ T cells and was also associated with decreased numbers of peritumoral CD8+ T cells, CD16+ macrophages, and CD163+ macrophages (p < 0.0001). The differences in the intratumor and peritumoral CD8+ T cell abundance were not impacted by neoadjuvant therapy, tumor stage, grade, lymph node status, histologic subtype, nor KRAS, p53, SMAD4, or CDKN2A mutations. CONCLUSIONS: Thus, K17 expression correlates with major differences in the immune microenvironment that are independent of any tested clinicopathologic or tumor intrinsic variables, suggesting that targeting K17-mediated immune effects on the immune system could restore the innate immunologic response to PDAC and might provide novel opportunities to restore immunotherapeutic approaches for this most deadly form of cancer
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
<h2>What's Changed</h2>
<ul>
<li>Version update for development by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/648</li>
<li>Added citation file by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/654</li>
<li>Added new optimizers by @AdiSir05 in https://github.com/mlcommons/GaNDLF/pull/646</li>
<li>Allow histology patches to be extracted without ground truth labels by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/657</li>
<li>Added metric calculation from CLI by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/663</li>
<li>Added a few segmentation metrics by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/661</li>
<li>Repository badges have been updated by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/667</li>
<li>Added instructions on creating new tutorials by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/664</li>
<li>Ensure parameters are built into the model dictionary by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/673</li>
<li>Calculating penalty after all compute objects are initialized by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/675</li>
<li>Add image similarity metrics by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/669</li>
<li>Allow the penalty and class weights in the config to be used by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/677</li>
<li>Added documentation related to OpenFL by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/683</li>
<li>Add MLCube wrapper for metrics API by @hasan7n in https://github.com/mlcommons/GaNDLF/pull/681</li>
<li>Adding mechanism to curate each extracted patch by @shubhaminnani in https://github.com/mlcommons/GaNDLF/pull/653</li>
<li>Added mask to SSIM function call by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/685</li>
<li>Removed history file by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/690</li>
<li>Updated the metrics output by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/687</li>
<li>Update docker image name in workflow by @hasan7n in https://github.com/mlcommons/GaNDLF/pull/692</li>
<li>Fixed plotting function for final stats by @Geeks-Sid in https://github.com/mlcommons/GaNDLF/pull/691</li>
<li>Fixed import for collect stats by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/694</li>
<li>HED augmentations for digital pathology image by @Geeks-Sid in https://github.com/mlcommons/GaNDLF/pull/649</li>
<li>Added focal loss by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/696</li>
<li>Added a temporary fix for protobuf by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/702</li>
<li>Use torchmetric PSNR implementation and argument ordering by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/693</li>
<li>Introduced percentile normalization for synthesis challenge metrics by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/700</li>
<li>Upgrade openvino version to latest by @Geeks-Sid in https://github.com/mlcommons/GaNDLF/pull/699</li>
<li>Additional PSNR evaluations for the normalized synthesis case by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/703</li>
<li>Improved formatting by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/707</li>
<li>Updated checkout version and test names for clarity by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/708</li>
<li>Updated default options for sgd by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/709</li>
<li>Added matthews correlation coefficient loss by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/706</li>
<li>Using tuples for PSNR datarange by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/712</li>
<li>Deploy model entrypoint by @hasan7n in https://github.com/mlcommons/GaNDLF/pull/711</li>
<li>Added parameter to toggle NCC computation by @FelixSteinbauer in https://github.com/mlcommons/GaNDLF/pull/717</li>
<li>Adding second classification tutorial by @vavali08 in https://github.com/mlcommons/GaNDLF/pull/698</li>
<li>Minor code refactoring by @tosemml in https://github.com/mlcommons/GaNDLF/pull/719</li>
<li>Combined writing and temp file creation in a single step by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/720</li>
<li>Update usage information for anonymizer by @sanashah007 in https://github.com/mlcommons/GaNDLF/pull/716</li>
<li>Move unit testing data to the mlcommons storage by @sarthakpati in https://github.com/mlcommons/GaNDLF/pull/722</li>
<li>Fixed model saving when git repo not found by @scap3yvt in https://github.com/mlcommons/GaNDLF/pull/729</li>
<li>Removing dev from version for tagging by @scap3yvt in https://github.com/mlcommons/GaNDLF/pull/731</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li>@AdiSir05 made their first contribution in https://github.com/mlcommons/GaNDLF/pull/646</li>
<li>@shubhaminnani made their first contribution in https://github.com/mlcommons/GaNDLF/pull/653</li>
<li>@FelixSteinbauer made their first contribution in https://github.com/mlcommons/GaNDLF/pull/685</li>
<li>@vavali08 made their first contribution in https://github.com/mlcommons/GaNDLF/pull/698</li>
<li>@tosemml made their first contribution in https://github.com/mlcommons/GaNDLF/pull/719</li>
<li>@sanashah007 made their first contribution in https://github.com/mlcommons/GaNDLF/pull/716</li>
<li>@scap3yvt made their first contribution in https://github.com/mlcommons/GaNDLF/pull/729</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/mlcommons/GaNDLF/compare/0.0.16...0.0.17</p>If you use this software, please cite it using this manuscript