21 research outputs found
Optimizing PatchCore for Few/many-shot Anomaly Detection
Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and
tries to distinguish between normal and anomalous data using only few selected
samples. While newly proposed few-shot AD methods do compare against
pre-existing algorithms developed for the full-shot domain as baselines, they
do not dedicatedly optimize them for the few-shot setting. It thus remains
unclear if the performance of such pre-existing algorithms can be further
improved. We address said question in this work. Specifically, we present a
study on the AD/anomaly segmentation (AS) performance of PatchCore, the current
state-of-the-art full-shot AD/AS algorithm, in both the few-shot and the
many-shot settings. We hypothesize that further performance improvements can be
realized by (I) optimizing its various hyperparameters, and by (II)
transferring techniques known to improve few-shot supervised learning to the AD
domain. Exhaustive experiments on the public VisA and MVTec AD datasets reveal
that (I) significant performance improvements can be realized by optimizing
hyperparameters such as the underlying feature extractor, and that (II)
image-level augmentations can, but are not guaranteed, to improve performance.
Based on these findings, we achieve a new state of the art in few-shot AD on
VisA, further demonstrating the merit of adapting pre-existing AD/AS methods to
the few-shot setting. Last, we identify the investigation of feature extractors
with a strong inductive bias as a potential future research direction for
(few-shot) AD/AS
The Medical Segmentation Decathlon
International challenges have become the de facto standard for comparative
assessment of image analysis algorithms given a specific task. Segmentation is
so far the most widely investigated medical image processing task, but the
various segmentation challenges have typically been organized in isolation,
such that algorithm development was driven by the need to tackle a single
specific clinical problem. We hypothesized that a method capable of performing
well on multiple tasks will generalize well to a previously unseen task and
potentially outperform a custom-designed solution. To investigate the
hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a
biomedical image analysis challenge, in which algorithms compete in a multitude
of both tasks and modalities. The underlying data set was designed to explore
the axis of difficulties typically encountered when dealing with medical
images, such as small data sets, unbalanced labels, multi-site data and small
objects. The MSD challenge confirmed that algorithms with a consistent good
performance on a set of tasks preserved their good average performance on a
different set of previously unseen tasks. Moreover, by monitoring the MSD
winner for two years, we found that this algorithm continued generalizing well
to a wide range of other clinical problems, further confirming our hypothesis.
Three main conclusions can be drawn from this study: (1) state-of-the-art image
segmentation algorithms are mature, accurate, and generalize well when
retrained on unseen tasks; (2) consistent algorithmic performance across
multiple tasks is a strong surrogate of algorithmic generalizability; (3) the
training of accurate AI segmentation models is now commoditized to non AI
experts
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
Current state-of-the-art anomaly detection (AD) methods exploit the powerful
representations yielded by large-scale ImageNet training. However, catastrophic
forgetting prevents the successful fine-tuning of pre-trained representations
on new datasets in the semi-supervised setting, and representations are
therefore commonly fixed. In our work, we propose a new method to overcome
catastrophic forgetting and thus successfully fine-tune pre-trained
representations for AD in the transfer learning setting. Specifically, we
induce a multivariate Gaussian distribution for the normal class based on the
linkage between generative and discriminative modeling, and use the Mahalanobis
distance of normal images to the estimated distribution as training objective.
We additionally propose to use augmentations commonly employed for vicinal risk
minimization in a validation scheme to detect onset of catastrophic forgetting.
Extensive evaluations on the public MVTec dataset reveal that a new state of
the art is achieved by our method in the AD task while simultaneously achieving
anomaly segmentation performance comparable to prior state of the art. Further,
ablation studies demonstrate the importance of the induced Gaussian
distribution as well as the robustness of the proposed fine-tuning scheme with
respect to the choice of augmentations.Comment: Camera ready for IMPROVE22 + additional typo fixe