5 research outputs found
Taming Detection Transformers for Medical Object Detection
The accurate detection of suspicious regions in medical images is an
error-prone and time-consuming process required by many routinely performed
diagnostic procedures. To support clinicians during this difficult task,
several automated solutions were proposed relying on complex methods with many
hyperparameters. In this study, we investigate the feasibility of DEtection
TRansformer (DETR) models for volumetric medical object detection. In contrast
to previous works, these models directly predict a set of objects without
relying on the design of anchors or manual heuristics such as
non-maximum-suppression to detect objects. We show by conducting extensive
experiments with three models, namely DETR, Conditional DETR, and DINO DETR on
four data sets (CADA, RibFrac, KiTS19, and LIDC) that these set prediction
models can perform on par with or even better than currently existing methods.
DINO DETR, the best-performing model in our experiments demonstrates this by
outperforming a strong anchor-based one-stage detector, Retina U-Net, on three
out of four data sets.Comment: BVM 2023 Oral. Marc K. Ickler and Michael Baumgartner contributed
equall
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
The medical imaging community generates a wealth of datasets, many of which
are openly accessible and annotated for specific diseases and tasks such as
multi-organ or lesion segmentation. Current practices continue to limit model
training and supervised pre-training to one or a few similar datasets,
neglecting the synergistic potential of other available annotated data. We
propose MultiTalent, a method that leverages multiple CT datasets with diverse
and conflicting class definitions to train a single model for a comprehensive
structure segmentation. Our results demonstrate improved segmentation
performance compared to previous related approaches, systematically, also
compared to single dataset training using state-of-the-art methods, especially
for lesion segmentation and other challenging structures. We show that
MultiTalent also represents a powerful foundation model that offers a superior
pre-training for various segmentation tasks compared to commonly used
supervised or unsupervised pre-training baselines. Our findings offer a new
direction for the medical imaging community to effectively utilize the wealth
of available data for improved segmentation performance. The code and model
weights will be published here: [tba]Comment: Accepted for Miccai 2023 and selected for an ora
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Despite the remarkable success of deep learning systems over the last decade,
a key difference still remains between neural network and human
decision-making: As humans, we cannot only form a decision on the spot, but
also ponder, revisiting an initial guess from different angles, distilling
relevant information, arriving at a better decision. Here, we propose
RecycleNet, a latent feature recycling method, instilling the pondering
capability for neural networks to refine initial decisions over a number of
recycling steps, where outputs are fed back into earlier network layers in an
iterative fashion. This approach makes minimal assumptions about the neural
network architecture and thus can be implemented in a wide variety of contexts.
Using medical image segmentation as the evaluation environment, we show that
latent feature recycling enables the network to iteratively refine initial
predictions even beyond the iterations seen during training, converging towards
an improved decision. We evaluate this across a variety of segmentation
benchmarks and show consistent improvements even compared with top-performing
segmentation methods. This allows trading increased computation time for
improved performance, which can be beneficial, especially for safety-critical
applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision
(WACV
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Classification of heterogeneous diseases is challenging due to their
complexity, variability of symptoms and imaging findings. Chronic Obstructive
Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being
the third leading cause of death. Its sparse, diffuse and heterogeneous
appearance on computed tomography challenges supervised binary classification.
We reformulate COPD binary classification as an anomaly detection task,
proposing cOOpD: heterogeneous pathological regions are detected as
Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we
learn representations of unlabeled lung regions employing a self-supervised
contrastive pretext model, potentially capturing specific characteristics of
diseased and healthy unlabeled regions. A generative model then learns the
distribution of healthy representations and identifies abnormalities (stemming
from COPD) as deviations. Patient-level scores are obtained by aggregating
region OOD scores. We show that cOOpD achieves the best performance on two
public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared
to the previous supervised state-of-the-art. Additionally, cOOpD yields
well-interpretable spatial anomaly maps and patient-level scores which we show
to be of additional value in identifying individuals in the early stage of
progression. Experiments in artificially designed real-world prevalence
settings further support that anomaly detection is a powerful way of tackling
COPD classification
Data_Sheet_1_Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.docx
BackgroundChronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD.MethodsNon-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1–4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated.ResultsInitial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p Emph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41–0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p fSAD (r = 0.61, p ConclusionOur study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct – yet complementary – strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.</p