2 research outputs found
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that
contains a duct-level instance segmentation model, a tissue-level semantic
segmentation model, and three-levels of features for diagnostic classification.
Based on recent advancements in instance segmentation and the Mask R-CNN model,
our duct-level segmenter tries to identify each ductal individual inside a
microscopic image; then, it extracts tissue-level information from the
identified ductal instances. Leveraging three levels of information obtained
from these ductal instances and also the histopathology image, the proposed
DIOP outperforms previous approaches (both feature-based and CNN-based) in all
diagnostic tasks; for the four-way classification task, the DIOP achieves
comparable performance to general pathologists in this unique dataset. The
proposed DIOP only takes a few seconds to run in the inference time, which
could be used interactively on most modern computers. More clinical
explorations are needed to study the robustness and generalizability of this
system in the future.Comment: ICPR 2020. Submitted July 15th, 202
Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
Prostate cancer is the most prevalent cancer among men in Western countries,
with 1.1 million new diagnoses every year. The gold standard for the diagnosis
of prostate cancer is a pathologists' evaluation of prostate tissue.
To potentially assist pathologists deep-learning-based cancer detection
systems have been developed. Many of the state-of-the-art models are
patch-based convolutional neural networks, as the use of entire scanned slides
is hampered by memory limitations on accelerator cards. Patch-based systems
typically require detailed, pixel-level annotations for effective training.
However, such annotations are seldom readily available, in contrast to the
clinical reports of pathologists, which contain slide-level labels. As such,
developing algorithms which do not require manual pixel-wise annotations, but
can learn using only the clinical report would be a significant advancement for
the field.
In this paper, we propose to use a streaming implementation of convolutional
layers, to train a modern CNN (ResNet-34) with 21 million parameters end-to-end
on 4712 prostate biopsies. The method enables the use of entire biopsy images
at high-resolution directly by reducing the GPU memory requirements by 2.4 TB.
We show that modern CNNs, trained using our streaming approach, can extract
meaningful features from high-resolution images without additional heuristics,
reaching similar performance as state-of-the-art patch-based and
multiple-instance learning methods. By circumventing the need for manual
annotations, this approach can function as a blueprint for other tasks in
histopathological diagnosis.
The source code to reproduce the streaming models is available at
https://github.com/DIAGNijmegen/pathology-streaming-pipeline