FastPathology: An open-source platform for deep learning-based research
and decision support in digital pathology
- URL: http://arxiv.org/abs/2011.06033v1
- Date: Wed, 11 Nov 2020 19:35:31 GMT
- Title: FastPathology: An open-source platform for deep learning-based research
and decision support in digital pathology
- Authors: Andr\'e Pedersen, Marit Valla, Anna M. Bofin, Javier P\'erez de
Frutos, Ingerid Reinertsen and Erik Smistad
- Abstract summary: There are several open-source platforms for working with whole-slide microscopy images, but few support deployment of CNN models.
We have developed a new platform, FastPathology, using the FAST framework and C++.
It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results.
- Score: 0.678543866474958
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep convolutional neural networks (CNNs) are the current state-of-the-art
for digital analysis of histopathological images. The large size of whole-slide
microscopy images (WSIs) requires advanced memory handling to read, display and
process these images. There are several open-source platforms for working with
WSIs, but few support deployment of CNN models. These applications use
third-party solutions for inference, making them less user-friendly and
unsuitable for high-performance image analysis. To make deployment of CNNs
user-friendly and feasible on low-end machines, we have developed a new
platform, FastPathology, using the FAST framework and C++. It minimizes memory
usage for reading and processing WSIs, deployment of CNN models, and real-time
interactive visualization of results. Runtime experiments were conducted on
four different use cases, using different architectures, inference engines,
hardware configurations and operating systems. Memory usage for reading,
visualizing, zooming and panning a WSI were measured, using FastPathology and
three existing platforms. FastPathology performed similarly in terms of memory
to the other C++ based application, while using considerably less than the two
Java-based platforms. The choice of neural network model, inference engine,
hardware and processors influenced runtime considerably. Thus, FastPathology
includes all steps needed for efficient visualization and processing of WSIs in
a single application, including inference of CNNs with real-time display of the
results. Source code, binary releases and test data can be found online on
GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.
Related papers
- DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit
CNNs [53.82853297675979]
1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices.
One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS.
We introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs.
arXiv Detail & Related papers (2023-06-27T11:28:29Z) - Slideflow: Deep Learning for Digital Histopathology with Real-Time
Whole-Slide Visualization [49.62449457005743]
We develop a flexible deep learning library for histopathology called Slideflow.
It supports a broad array of deep learning methods for digital pathology.
It includes a fast whole-slide interface for deploying trained models.
arXiv Detail & Related papers (2023-04-09T02:49:36Z) - HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D
Medical Image Segmentation using HyperNet [51.60655410423093]
We introduce HyperSegNAS to enable one-shot Neural Architecture Search (NAS) for medical image segmentation.
We show that HyperSegNAS yields better performing and more intuitive architectures compared to the previous state-of-the-art (SOTA) segmentation networks.
Our method is evaluated on public datasets from the Medical Decathlon (MSD) challenge, and achieves SOTA performances.
arXiv Detail & Related papers (2021-12-20T16:21:09Z) - An Acceleration Method Based on Deep Learning and Multilinear Feature
Space [0.0]
This paper presents an alternative approach based on the Multilinear Feature Space (MFS) method resorting to transfer learning from large CNN architectures.
The proposed method uses CNNs to generate feature maps, although it does not work as complexity reduction approach.
Our method, named AMFC, uses the transfer learning from pre-trained CNN to reduce the classification time of new sample image, with minimal accuracy loss.
arXiv Detail & Related papers (2021-10-16T23:49:12Z) - HistoTransfer: Understanding Transfer Learning for Histopathology [9.231495418218813]
We compare the performance of features extracted from networks trained on ImageNet and histopathology data.
We investigate if features learned using more complex networks lead to gain in performance.
arXiv Detail & Related papers (2021-06-13T18:55:23Z) - PocketNet: A Smaller Neural Network for 3D Medical Image Segmentation [0.0]
We derive a new CNN architecture called PocketNet that achieves comparable segmentation results to conventional CNNs while using less than 3% of the number of parameters.
We show that PocketNet achieves comparable segmentation results to conventional CNNs while using less than 3% of the number of parameters.
arXiv Detail & Related papers (2021-04-21T20:10:30Z) - The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer.
Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
arXiv Detail & Related papers (2021-01-29T07:46:39Z) - CNNs for JPEGs: A Study in Computational Cost [49.97673761305336]
Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade.
CNNs are capable of learning robust representations of the data directly from the RGB pixels.
Deep learning methods capable of learning directly from the compressed domain have been gaining attention in recent years.
arXiv Detail & Related papers (2020-12-26T15:00:10Z) - Spatio-Temporal Inception Graph Convolutional Networks for
Skeleton-Based Action Recognition [126.51241919472356]
We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition.
Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
arXiv Detail & Related papers (2020-11-26T14:43:04Z) - FNA++: Fast Network Adaptation via Parameter Remapping and Architecture
Search [35.61441231491448]
We propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network.
In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation.
The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches.
arXiv Detail & Related papers (2020-06-21T10:03:34Z) - Fast Neural Network Adaptation via Parameter Remapping and Architecture
Search [35.61441231491448]
Deep neural networks achieve remarkable performance in many computer vision tasks.
Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone.
One major challenge though, is that ImageNet pre-training of the search space representation incurs huge computational cost.
In this paper, we propose a Fast Neural Network Adaptation (FNA) method, which can adapt both the architecture and parameters of a seed network.
arXiv Detail & Related papers (2020-01-08T13:45:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.