97 research outputs found
Efficient Image Evidence Analysis of CNN Classification Results
Convolutional neural networks (CNNs) define the current state-of-the-art for
image recognition. With their emerging popularity, especially for critical
applications like medical image analysis or self-driving cars, confirmability
is becoming an issue. The black-box nature of trained predictors make it
difficult to trace failure cases or to understand the internal reasoning
processes leading to results. In this paper we introduce a novel efficient
method to visualise evidence that lead to decisions in CNNs. In contrast to
network fixation or saliency map methods, our method is able to illustrate the
evidence for or against a classifier's decision in input pixel space
approximately 10 times faster than previous methods. We also show that our
approach is less prone to noise and can focus on the most relevant input
regions, thus making it more accurate and interpretable. Moreover, by making
simplifications we link our method with other visualisation methods, providing
a general explanation for gradient-based visualisation techniques. We believe
that our work makes network introspection more feasible for debugging and
understanding deep convolutional networks. This will increase trust between
humans and deep learning models.Comment: 14 pages, 19 figure
Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference
Applying ML advancements to healthcare can improve patient outcomes. However,
the sheer operational complexity of ML models, combined with legacy hardware
and multi-modal gigapixel images, poses a severe deployment limitation for
real-time, on-device inference. We consider filter pruning as a solution,
exploring segmentation models in cardiology and ophthalmology. Our preliminary
results show a compression rate of up to 1148x with minimal loss in quality,
stressing the need to consider task complexity and architectural details when
using off-the-shelf models. At high compression rates, filter-pruned models
exhibit faster inference on a CPU than the GPU baseline. We also demonstrate
that such models' robustness and generalisability characteristics exceed that
of the baseline and weight-pruned counterparts. We uncover intriguing questions
and take a step towards realising cost-effective disease diagnosis, monitoring,
and preventive solutions
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting
Recent advances in score-based generative models have led to a huge spike in
the development of downstream applications using generative models ranging from
data augmentation over image and video generation to anomaly detection. Despite
publicly available trained models, their potential to be used for privacy
preserving data sharing has not been fully explored yet. Training diffusion
models on private data and disseminating the models and weights rather than the
raw dataset paves the way for innovative large-scale data-sharing strategies,
particularly in healthcare, where safeguarding patients' personal health
information is paramount. However, publishing such models without individual
consent of, e.g., the patients from whom the data was acquired, necessitates
guarantees that identifiable training samples will never be reproduced, thus
protecting personal health data and satisfying the requirements of policymakers
and regulatory bodies. This paper introduces a method for estimating the upper
bound of the probability of reproducing identifiable training images during the
sampling process. This is achieved by designing an adversarial approach that
searches for anatomic fingerprints, such as medical devices or dermal art,
which could potentially be employed to re-identify training images. Our method
harnesses the learned score-based model to estimate the probability of the
entire subspace of the score function that may be utilized for one-to-one
reproduction of training samples. To validate our estimates, we generate
anomalies containing a fingerprint and investigate whether generated samples
from trained generative models can be uniquely mapped to the original training
samples. Overall our results show that privacy-breaching images are reproduced
at sampling time if the models were trained without care.Comment: 10 pages, 6 figure
Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation
This work presents an extensive hyperparameter search on Image Diffusion
Models for Echocardiogram generation. The objective is to establish
foundational benchmarks and provide guidelines within the realm of ultrasound
image and video generation. This study builds over the latest advancements,
including cutting-edge model architectures and training methodologies. We also
examine the distribution shift between real and generated samples and consider
potential solutions, crucial to train efficient models on generated data. We
determine an Optimal FID score of for our research problem and achieve
an FID of . This work is aimed at contributing valuable insights and
serving as a reference for further developments in the specialized field of
ultrasound image and video generation.Comment: MedNeurIPS 2023 poste
A Review of Causality for Learning Algorithms in Medical Image Analysis
Medical image analysis is a vibrant research area that offers doctors and
medical practitioners invaluable insight and the ability to accurately diagnose
and monitor disease. Machine learning provides an additional boost for this
area. However, machine learning for medical image analysis is particularly
vulnerable to natural biases like domain shifts that affect algorithmic
performance and robustness. In this paper we analyze machine learning for
medical image analysis within the framework of Technology Readiness Levels and
review how causal analysis methods can fill a gap when creating robust and
adaptable medical image analysis algorithms. We review methods using causality
in medical imaging AI/ML and find that causal analysis has the potential to
mitigate critical problems for clinical translation but that uptake and
clinical downstream research has been limited so far.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA)
https://www.melba-journal.org/papers/2022:028.html". ; Paper ID: 2022:02
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task. With the
advent of large-scale pre-trained generative models, conditional image
generation has been given a significant boost in result quality and ease of
use. In this paper, we present a novel method that enables the generation of
general foreground-background segmentation models from simple textual
descriptions, without requiring segmentation labels. We leverage and explore
pre-trained latent diffusion models, to automatically generate weak
segmentation masks for concepts and objects. The masks are then used to
fine-tune the diffusion model on an inpainting task, which enables fine-grained
removal of the object, while at the same time providing a synthetic foreground
and background dataset. We demonstrate that using this method beats previous
methods in both discriminative and generative performance and closes the gap
with fully supervised training while requiring no pixel-wise object labels. We
show results on the task of segmenting four different objects (humans, dogs,
cars, birds) and a use case scenario in medical image analysis. The code is
available at https://github.com/MischaD/fobadiffusion.Comment: Accepted at ICCV202
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