9 research outputs found
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
Annotating medical imaging datasets is costly, so fine-tuning (or transfer
learning) is the most effective method for digital pathology vision
applications such as disease classification and semantic segmentation. However,
due to texture bias in models trained on real-world images, transfer learning
for histopathology applications might result in underperforming models, which
necessitates the need for using unlabeled histopathology data and
self-supervised methods to discover domain-specific characteristics. Here, we
tested the premise that histopathology-specific pretrained models provide
better initializations for pathology vision tasks, i.e., gland and cell
segmentation. In this study, we compare the performance of gland and cell
segmentation tasks with domain-specific and non-domain-specific pretrained
weights. Moreover, we investigate the data size at which domain-specific
pretraining produces a statistically significant difference in performance. In
addition, we investigated whether domain-specific initialization improves the
effectiveness of out-of-domain testing on distinct datasets but the same task.
The results indicate that performance gain using domain-specific pretraining
depends on both the task and the size of the training dataset. In instances
with limited dataset sizes, a significant improvement in gland segmentation
performance was also observed, whereas models trained on cell segmentation
datasets exhibit no improvement
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
Statistical shape models (SSM) have been well-established as an excellent
tool for identifying variations in the morphology of anatomy across the
underlying population. Shape models use consistent shape representation across
all the samples in a given cohort, which helps to compare shapes and identify
the variations that can detect pathologies and help in formulating treatment
plans. In medical imaging, computing these shape representations from CT/MRI
scans requires time-intensive preprocessing operations, including but not
limited to anatomy segmentation annotations, registration, and texture
denoising. Deep learning models have demonstrated exceptional capabilities in
learning shape representations directly from volumetric images, giving rise to
highly effective and efficient Image-to-SSM. Nevertheless, these models are
data-hungry and due to the limited availability of medical data, deep learning
models tend to overfit. Offline data augmentation techniques, that use kernel
density estimation based (KDE) methods for generating shape-augmented samples,
have successfully aided Image-to-SSM networks in achieving comparable accuracy
to traditional SSM methods. However, these augmentation methods focus on shape
augmentation, whereas deep learning models exhibit image-based texture bias
results in sub-optimal models. This paper introduces a novel strategy for
on-the-fly data augmentation for the Image-to-SSM framework by leveraging
data-dependent noise generation or texture augmentation. The proposed framework
is trained as an adversary to the Image-to-SSM network, augmenting diverse and
challenging noisy samples. Our approach achieves improved accuracy by
encouraging the model to focus on the underlying geometry rather than relying
solely on pixel values
Multi-Set Inoculation: Assessing Model Robustness Across Multiple Challenge Sets
Language models, given their black-box nature, often exhibit sensitivity to
input perturbations, leading to trust issues due to hallucinations. To bolster
trust, it's essential to understand these models' failure modes and devise
strategies to enhance their performance. In this study, we propose a framework
to study the effect of input perturbations on language models of different
scales, from pre-trained models to large language models (LLMs). We use
fine-tuning to train a robust model to perturbations, and we investigate
whether exposure to one perturbation improves or degrades the model's
performance on other perturbations. To address multi-perturbation robustness,
we suggest three distinct training strategies. We also extend the framework to
LLMs via a chain of thought(COT) prompting with exemplars. We instantiate our
framework for the Tabular-NLI task and show that the proposed strategies train
the model robust to different perturbations without losing accuracy on a given
dataset.Comment: 13 pages, 2 Figure, 12 Table
InfoSync: Information Synchronization across Multilingual Semi-structured Tables
Information Synchronization of semi-structured data across languages is
challenging. For instance, Wikipedia tables in one language should be
synchronized across languages. To address this problem, we introduce a new
dataset InfoSyncC and a two-step method for tabular synchronization. InfoSync
contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages,
of which a subset (3.5K pairs) are manually annotated. The proposed method
includes 1) Information Alignment to map rows and 2) Information Update for
updating missing/outdated information for aligned tables across multilingual
tables. When evaluated on InfoSync, information alignment achieves an F1 score
of 87.91 (en non-en). To evaluate information updation, we perform
human-assisted Wikipedia edits on Infoboxes for 603 table pairs. Our approach
obtains an acceptance rate of 77.28% on Wikipedia, showing the effectiveness of
the proposed method.Comment: 22 pages, 7 figures, 20 tables, ACL 2023 (Toronto, Canada
Unsupervised Domain Adaptation for Semantic Segmentation via Feature-space Density Matching
Semantic segmentation is a critical step in automated image interpretation
and analysis where pixels are classified into one or more predefined
semantically meaningful classes. Deep learning approaches for semantic
segmentation rely on harnessing the power of annotated images to learn features
indicative of these semantic classes. Nonetheless, they often fail to
generalize when there is a significant domain (i.e., distributional) shift
between the training (i.e., source) data and the dataset(s) encountered when
deployed (i.e., target), necessitating manual annotations for the target data
to achieve acceptable performance. This is especially important in medical
imaging because different image modalities have significant intra- and
inter-site variations due to protocol and vendor variability. Current
techniques are sensitive to hyperparameter tuning and target dataset size. This
paper presents an unsupervised domain adaptation approach for semantic
segmentation that alleviates the need for annotating target data. Using kernel
density estimation, we match the target data distribution to the source data in
the feature space. We demonstrate that our results are comparable or superior
on multiple-site prostate MRI and histopathology images, which mitigates the
need for annotating target data