60 research outputs found
HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning
Deep learning has been effective for histology image analysis in digital
pathology. However, many current deep learning approaches require large,
strongly- or weakly-labeled images and regions of interest, which can be
time-consuming and resource-intensive to obtain. To address this challenge, we
present HistoPerm, a view generation method for representation learning using
joint embedding architectures that enhances representation learning for
histology images. HistoPerm permutes augmented views of patches extracted from
whole-slide histology images to improve classification performance. We
evaluated the effectiveness of HistoPerm on two histology image datasets for
Celiac disease and Renal Cell Carcinoma, using three widely used joint
embedding architecture-based representation learning methods: BYOL, SimCLR, and
VICReg. Our results show that HistoPerm consistently improves patch- and
slide-level classification performance in terms of accuracy, F1-score, and AUC.
Specifically, for patch-level classification accuracy on the Celiac disease
dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal
Cell Carcinoma dataset, patch-level classification accuracy is increased by 2%
for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease
dataset, models with HistoPerm outperform the fully-supervised baseline model
by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal
Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for
the models up to 10% relative to the fully-supervised baseline. These findings
suggest that HistoPerm can be a valuable tool for improving representation
learning of histopathology features when access to labeled data is limited and
can lead to whole-slide classification results that are comparable to or
superior to fully-supervised methods
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models
Large Language Models (LLMs) have significantly advanced the field of Natural
Language Processing (NLP), but their lack of interpretability has been a major
concern. Current methods for interpreting LLMs are post hoc, applied after
inference time, and have limitations such as their focus on low-level features
and lack of explainability at higher level text units. In this work, we
introduce proto-lm, a prototypical network-based white-box framework that
allows LLMs to learn immediately interpretable embeddings during the
fine-tuning stage while maintaining competitive performance. Our method's
applicability and interpretability are demonstrated through experiments on a
wide range of NLP tasks, and our results indicate a new possibility of creating
interpretable models without sacrificing performance. This novel approach to
interpretability in LLMs can pave the way for more interpretable models without
the need to sacrifice performance.Comment: Accepted to the Findings of EMNLP 202
Computational Complexity of Bi-clustering.
Bi-clustering, i.e. simultaneously clustering the rows and columns of matrices based on their entries, covers a large variety of techniques in data mining. The goal of all bi-clustering techniques is finding the partitions of the rows and the columns in which sub-rows and sub-columns show a similar behavior. Currently existing algorithms for bi-clustering problems are either heuristic, or try to solve approximations of the original problems. There is no efficient algorithm for exact bi-clustering problems.
The computational complexity of bi-clustering problems depends on the exact problem formulation, and particularly on the merit function used to evaluate the quality of a given bi-clustering partition. The computational complexity of most of the common bi-clustering problems is unknown. In this thesis, we present a formal definition for the homogeneous cover problem. This problem has many applications from bio-informatics to targeted marketing. We analyze its computational complexity and show that the problem is NP-hard
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas
Improving Representation Learning for Histopathologic Images with Cluster Constraints
Recent advances in whole-slide image (WSI) scanners and computational
capabilities have significantly propelled the application of artificial
intelligence in histopathology slide analysis. While these strides are
promising, current supervised learning approaches for WSI analysis come with
the challenge of exhaustively labeling high-resolution slides - a process that
is both labor-intensive and time-consuming. In contrast, self-supervised
learning (SSL) pretraining strategies are emerging as a viable alternative,
given that they don't rely on explicit data annotations. These SSL strategies
are quickly bridging the performance disparity with their supervised
counterparts. In this context, we introduce an SSL framework. This framework
aims for transferable representation learning and semantically meaningful
clustering by synergizing invariance loss and clustering loss in WSI analysis.
Notably, our approach outperforms common SSL methods in downstream
classification and clustering tasks, as evidenced by tests on the Camelyon16
and a pancreatic cancer dataset.Comment: Accepted by ICCV202
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