1,114 research outputs found
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.Comment: Accepted by NeurIPS 202
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Analyzing high resolution whole slide images (WSIs) with regard to
information across multiple scales poses a significant challenge in digital
pathology. Multi-instance learning (MIL) is a common solution for working with
high resolution images by classifying bags of objects (i.e. sets of smaller
image patches). However, such processing is typically performed at a single
scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale
information that is key to diagnoses by human pathologists. In this study, we
propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale
relationships into a single MIL network for pathological image diagnosis. The
contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL)
algorithm that integrates the multi-scale information and the inter-scale
relationships is proposed; (2) A toy dataset with scale-specific morphological
features is created and released to examine and visualize differential
cross-scale attention; (3) Superior performance on both in-house and public
datasets is demonstrated by our simple cross-scale MIL strategy. The official
implementation is publicly available at https://github.com/hrlblab/CS-MIL
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
A method for automatically quantifying emphysema regions using
High-Resolution Computed Tomography (HRCT) scans of patients with chronic
obstructive pulmonary disease (COPD) that does not require manually annotated
scans for training is presented. HRCT scans of controls and of COPD patients
with diverse disease severity are acquired at two different centers. Textural
features from co-occurrence matrices and Gaussian filter banks are used to
characterize the lung parenchyma in the scans. Two robust versions of multiple
instance learning (MIL) classifiers, miSVM and MILES, are investigated. The
classifiers are trained with the weak labels extracted from the forced
expiratory volume in one minute (FEV) and diffusing capacity of the lungs
for carbon monoxide (DLCO). At test time, the classifiers output a patient
label indicating overall COPD diagnosis and local labels indicating the
presence of emphysema. The classifier performance is compared with manual
annotations by two radiologists, a classical density based method, and
pulmonary function tests (PFTs). The miSVM classifier performed better than
MILES on both patient and emphysema classification. The classifier has a
stronger correlation with PFT than the density based method, the percentage of
emphysema in the intersection of annotations from both radiologists, and the
percentage of emphysema annotated by one of the radiologists. The correlation
between the classifier and the PFT is only outperformed by the second
radiologist. The method is therefore promising for facilitating assessment of
emphysema and reducing inter-observer variability.Comment: Accepted at PLoS ON
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
Deep neural network models can learn clinically relevant features from
millions of histopathology images. However generating high-quality annotations
to train such models for each hospital, each cancer type, and each diagnostic
task is prohibitively laborious. On the other hand, terabytes of training data
-- while lacking reliable annotations -- are readily available in the public
domain in some cases. In this work, we explore how these large datasets can be
consciously utilized to pre-train deep networks to encode informative
representations. We then fine-tune our pre-trained models on a fraction of
annotated training data to perform specific downstream tasks. We show that our
approach can reach the state-of-the-art (SOTA) for patch-level classification
with only 1-10% randomly selected annotations compared to other SOTA
approaches. Moreover, we propose an uncertainty-aware loss function, to
quantify the model confidence during inference. Quantified uncertainty helps
experts select the best instances to label for further training. Our
uncertainty-aware labeling reaches the SOTA with significantly fewer
annotations compared to random labeling. Last, we demonstrate how our
pre-trained encoders can surpass current SOTA for whole-slide image
classification with weak supervision. Our work lays the foundation for data and
task-agnostic pre-trained deep networks with quantified uncertainty.Comment: 18 pages, 8 figure
- …