Ravi Kant Gupta

Ardhendu Sekhar, Vrinda Goel, Garima Jain et al.

The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and accurately predicting HER2 status using H\&E could reduce costs and expedite treatment selection. Deep Learning algorithms for H&E have shown effectiveness in predicting various cancer features and clinical outcomes, including moderate success in HER2 status prediction. In this work, we employed a customized weak supervision classification technique combined with MoCo-v2 contrastive learning to predict HER2 status. We trained our pipeline on 182 publicly available H&E Whole Slide Images (WSIs) from The Cancer Genome Atlas (TCGA), for which annotations by the pathology team at Yale School of Medicine are publicly available. Our pipeline achieved an Area Under the Curve (AUC) of 0.85 across four different test folds. Additionally, we tested our model on 44 H&E slides from the TCGA-BRCA dataset, which had an HER2 score of 2+ and included corresponding HER2 status and FISH test results. These cases are considered equivocal for IHC, requiring an expensive FISH test on their IHC slides for disambiguation. Our pipeline demonstrated an AUC of 0.81 on these challenging H&E slides. Reducing the need for FISH test can have significant implications in cancer treatment equity for underserved populations.

Ravi Kant Gupta, Shounak Das, Amit Sethi

This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. The objective is to enhance domain alignment and reduce domain shifts between these domains by leveraging their unique characteristics. Our approach proposes a novel loss function along with carefully selected existing loss functions tailored to address the challenges specific to histology images. This loss combination not only makes the model accurate and robust but also faster in terms of training convergence. We specifically focus on leveraging histology-specific features, such as tissue structure and cell morphology, to enhance adaptation performance in the histology domain. The proposed method is extensively evaluated in accuracy, robustness, and generalization, surpassing state-of-the-art techniques for histology images. We conducted extensive experiments on the FHIST dataset and the results show that our proposed method - Domain Adaptive Learning (DAL) significantly surpasses the ViT-based and CNN-based SoTA methods by 1.41% and 6.56% respectively.

Ravi Kant Gupta, Shounak Das, Amit Sethi

We present a novel approach for unsupervised domain adaptation (UDA) for natural images. A commonly-used objective for UDA schemes is to enhance domain alignment in representation space even if there is a domain shift in the input space. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. Our approach has two main features. Firstly, its neural architecture uses the deep structure of ResNet and the effective separation of scales of feature pyramidal network (FPN) to work with both content and style features. Secondly, it uses a combination of a novel loss function and judiciously selected existing loss functions to train the network architecture. This tailored combination is designed to address challenges inherent to natural images, such as scale, noise, and style shifts, that occur on top of a multi-modal (multi-class) distribution. The combined loss function not only enhances model accuracy and robustness on the target domain but also speeds up training convergence. Our proposed UDA scheme generalizes better than state-of-the-art for CNN-based methods on Office-Home, Office-31, and VisDA-2017 datasets and comaparable for DomainNet dataset.

Garima Jain, Ravi Kant Gupta, Priyansh Jain et al.

In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges associated with label accuracy and data noise. We trained an initial binary classifier to distinguish between leukemic (blasts) and non-leukemic cells images, achieving 90 percent accuracy. To evaluate the models generalization, we applied this model to a separate large unlabeled dataset and validated the predictions with two haemato-pathologists, finding an approximate error rate of 20 percent in the leukemic and non-leukemic labels. Assuming this level of label noise, we further trained a four-class model on images predicted as blasts to classify specific mutations. The mutation labels were known for only a bag of cell images extracted from a single slide. Despite the tumor label noise, our mutation classification model achieved 85 percent accuracy across four mutation classes, demonstrating resilience to label inconsistencies. This study highlights the capability of machine learning models to work with noisy labels effectively while providing accurate, clinically relevant mutation predictions, which is promising for diagnostic applications in areas such as haemato-pathology.

Ravi Kant Gupta, Shounak Das, Ardhendu Sekhar et al.

Whole slide images (WSIs) are high-resolution, gigapixel sized images that pose significant computational challenges for traditional machine learning models due to their size and heterogeneity.In this paper, we present a scalable and efficient methodology for WSI classification by leveraging patch-based feature extraction, clustering, and Fisher vector encoding. Initially, WSIs are divided into fixed size patches, and deep feature embeddings are extracted from each patch using a pre-trained convolutional neural network (CNN). These patch-level embeddings are subsequently clustered using K-means clustering, where each cluster aggregates semantically similar regions of the WSI. To effectively summarize each cluster, Fisher vector representations are computed by modeling the distribution of patch embeddings in each cluster as a parametric Gaussian mixture model (GMM). The Fisher vectors from each cluster are concatenated into a high-dimensional feature vector, creating a compact and informative representation of the entire WSI. This feature vector is then used by a classifier to predict the WSI's diagnostic label. Our method captures local and global tissue structures and yields robust performance for large-scale WSI classification, demonstrating superior accuracy and scalability compared to other approaches.

Ravi Kant Gupta, Mohit Jindal, Garima Jain et al.

We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their distinct molecular profiles and treatment responses. Our proposed deep learning model demonstrates robust performance, achieving an average area under the curve (AUC) of (87.4 pm 5.7)\% during cross-validation. It shows a high positive predictive value (PPV), highlighting its potential for clinical application, such as triaging for molecular testing. To gain biological insights, we performed an analysis of morphological features of ABC and GCB subtypes. We segmented cell nuclei using a pre-trained deep neural network and compared the statistics of geometric and color features for ABC and GCB. We found that the distributions of these features were not very different for the two subtypes, which suggests that the visual differences between them are more subtle. These results underscore the potential of our method to assist in more precise subtype classification and can contribute to improved treatment management and outcomes for patients of DLBCL.

Ravi Kant Gupta, Shounak Das, Amit Sethi

Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field.

Ravi Kant Gupta, Dadi Dharani, Shambhavi Shanker et al.

The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches, thus eliminating the need to process the entire slide. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors (FVs) for representing features extracted from these patches, which is known for its robustness in capturing fine-grained details. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable. We have rigorously evaluated the proposed method across multiple datasets to benchmark its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The empirical results indicate that our focused analysis of select patches, combined with Fisher vector representation, not only aligns with, but at times surpasses, the classification accuracy of standard practices. Moreover, this strategy notably diminishes computational load and resource expenditure, thereby establishing an efficient and precise framework for WSI analysis in the realm of digital pathology.

Ardhendu Sekhar, Aditya Bhattacharya, Vinayak Goyal et al.

In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.