Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features

Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor-positive breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low and high risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 993 hematoxylin and eosin-stained whole-slide images of breast cancers with corresponding clinicopathological features that had prior Oncotype DX testing. The model's performance was evaluated using an internal test set of 198 patients from Dartmouth Health and an external test set of 418 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.92 (95 percent CI: 0.88-0.96) on the internal set and an AUC of 0.85 (95 percent CI: 0.79-0.90) on the external cohort. These results suggest that with further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes.

Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer

Endometrial cancer, the sixth most common cancer in females worldwide, presents as a heterogeneous group with certain types prone to recurrence. Precise histologic evaluation of endometrial cancer is essential for effective patient management and determining the best treatment modalities. This study introduces EndoNet, a transformer-based deep learning approach for histologic classification of endometrial cancer. EndoNet uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual characteristics. The model was trained on 929 digitized hematoxylin and eosin-stained whole slide images of endometrial cancer from hysterectomy cases at Dartmouth Health. It classifies these slides into low grade (Endometroid Grades 1 and 2) and high-grade (endometroid carcinoma FIGO grade 3, uterine serous carcinoma, carcinosarcoma) categories. EndoNet was evaluated on an internal test set of 218 slides and an external test set of 100 random slides from the public TCGA database. The model achieved a weighted average F1-score of 0.92 (95% CI: 0.87-0.95) and an AUC of 0.93 (95% CI: 0.88-0.96) on the internal test, and 0.86 (95% CI: 0.80-0.94) for F1-score and 0.86 (95% CI: 0.75-0.93) for AUC on the external test. Pending further validation, EndoNet has the potential to assist pathologists in classifying challenging gynecologic pathology tumors and enhancing patient care.

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.

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. The code and additional details are accessible at: https://github.com/wwyi1828/CluSiam.

Masked Pre-Training of Transformers for Histology Image Analysis

In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging. Inspired by masked language models, we propose a pretext task for training the transformer model without labeled data to address this problem. Our model, MaskHIT, uses the transformer output to reconstruct masked patches and learn representative histological features based on their positions and visual features. The experimental results demonstrate that MaskHIT surpasses various multiple instance learning approaches by 3% and 2% on survival prediction and cancer subtype classification tasks, respectively. Furthermore, MaskHIT also outperforms two of the most recent state-of-the-art transformer-based methods. Finally, a comparison between the attention maps generated by the MaskHIT model with pathologist's annotations indicates that the model can accurately identify clinically relevant histological structures in each task.

HistoPerm: A Permutation-Based View Generation Approach for Learning Histopathologic Feature Representations

Recently, deep learning methods have been successfully applied to solve numerous challenges in the field of digital pathology. However, many of these approaches are fully supervised and require annotated images. Annotating a histology image is a time-consuming and tedious process for even a highly skilled pathologist, and, as such, most histology datasets lack region-of-interest annotations and are weakly labeled. In this paper, we introduce HistoPerm, a view generation approach designed for improving the performance of representation learning techniques on histology images in weakly supervised settings. In HistoPerm, we permute augmented views of patches generated from whole-slide histology images to improve classification accuracy. These permuted views belong to the same original slide-level class but are produced from distinct patch instances. We tested adding HistoPerm to BYOL and SimCLR, two prominent representation learning methods, on two public histology datasets for Celiac disease and Renal Cell Carcinoma. For both datasets, we found improved performance in terms of accuracy, F1-score, and AUC compared to the standard BYOL and SimCLR approaches. Particularly, in a linear evaluation configuration, HistoPerm increases classification accuracy on the Celiac disease dataset by 8% for BYOL and 3% for SimCLR. Similarly, with HistoPerm, classification accuracy increases by 2% for BYOL and 0.25% for SimCLR on the Renal Cell Carcinoma dataset. The proposed permutation-based view generation approach can be adopted in common representation learning frameworks to capture histopathology features in weakly supervised settings and can lead to whole-slide classification outcomes that are close to, or even better than, fully supervised methods.

Interpretation Quality Score for Measuring the Quality of interpretability methods

Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their decisions has become apparent in many applications. To that end, many interpretability methods that help explain the decision processes of ML models have been developed. Yet, there currently exists no widely-accepted metric to evaluate the quality of explanations generated by these methods. As a result, there currently is no standard way of measuring to what degree an interpretability method achieves an intended objective. Moreover, there is no accepted standard of performance by which we can compare and rank the current existing interpretability methods. In this paper, we propose a novel metric for quantifying the quality of explanations generated by interpretability methods. We compute the metric on three NLP tasks using six interpretability methods and present our results.

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. In addition to desirable evaluation metrics, a high level of interpretability is often required for these models to be reliably utilized. Therefore, explanations that offer insight into the process by which a model maps its inputs onto its outputs are much sought-after. Unfortunately, current black box nature of machine learning models is still an unresolved issue and this very nature prevents researchers from learning and providing explicative descriptions for a model's behavior and final predictions. In this work, we propose a novel framework utilizing Adversarial Inverse Reinforcement Learning that can provide global explanations for decisions made by a Reinforcement Learning model and capture intuitive tendencies that the model follows by summarizing the model's decision-making process.

Calibrating Histopathology Image Classifiers using Label Smoothing

The classification of histopathology images fundamentally differs from traditional image classification tasks because histopathology images naturally exhibit a range of diagnostic features, resulting in a diverse range of annotator agreement levels. However, examples with high annotator disagreement are often either assigned the majority label or discarded entirely when training histopathology image classifiers. This widespread practice often yields classifiers that do not account for example difficulty and exhibit poor model calibration. In this paper, we ask: can we improve model calibration by endowing histopathology image classifiers with inductive biases about example difficulty? We propose several label smoothing methods that utilize per-image annotator agreement. Though our methods are simple, we find that they substantially improve model calibration, while maintaining (or even improving) accuracy. For colorectal polyp classification, a common yet challenging task in gastrointestinal pathology, we find that our proposed agreement-aware label smoothing methods reduce calibration error by almost 70%. Moreover, we find that using model confidence as a proxy for annotator agreement also improves calibration and accuracy, suggesting that datasets without multiple annotators can still benefit from our proposed label smoothing methods via our proposed confidence-aware label smoothing methods. Given the importance of calibration (especially in histopathology image analysis), the improvements from our proposed techniques merit further exploration and potential implementation in other histopathology image classification tasks.