31 citations · 89 across the 21 of their papers we have counts for
24 papers
PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
Azim Dehghani Amirabad, Junchao Zhu, Pushpak Pati +3
Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, e…
TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
Varun Belagali, Saarthak Kapse, Pierre Marza +12
The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer t…
MEGAN: Mixture of Experts for Robust Uncertainty Estimation in Endoscopy Videos
Damola Agbelese, Krishna Chaitanya, Pushpak Pati +11
Reliable uncertainty quantification (UQ) is essential in medical AI. Evidential Deep Learning (EDL) offers a computationally efficient way to quantify model uncertainty alongside p…
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
Amina Mollaysa, Artem Moskale, Pushpak Pati +3
We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogm…
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Saarthak Kapse, Pushpak Pati, Srikar Yellapragada +5
Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. W…
ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology
Vishwesh Ramanathan, Tony Xu, Pushpak Pati +3
Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data avai…
Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
M. Emre Sahin, Benjamin C. B. Symons, Pushpak Pati +5
Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel…
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Saarthak Kapse, Pushpak Pati, Srijan Das +7
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slid…
Hybrid quantum-classical graph neural networks for tumor classification in digital pathology
Anupama Ray, Dhiraj Madan, Srushti Patil +2
Advances in classical machine learning and single-cell technologies have paved the way to understand interactions between disease cells and tumor microenvironments to accelerate th…
Towards quantum-enabled cell-centric therapeutics
Saugata Basu, Jannis Born, Aritra Bose +30
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future qua…
Generative appearance replay for continual unsupervised domain adaptation
Boqi Chen, Kevin Thandiackal, Pushpak Pati +1
Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may beco…
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Kevin Thandiackal, Luigi Piccinelli, Pushpak Pati +1
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplin…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati +4
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate…
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Nadia Brancati, Anna Maria Anniciello, Pushpak Pati +10
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with lar…
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
Guillaume Jaume, Pushpak Pati, Valentin Anklin +2
Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relations…
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta +14
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entitie…
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Valentin Anklin, Pushpak Pati, Guillaume Jaume +6
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have…
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar +7
Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniqu…
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Gonglei Shi, Li Xiao, Yang Chen +1
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partial…
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Pushpak Pati, Antonio Foncubierta-Rodriguez, Orcun Goksel +1
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labe…
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Pushpak Pati, Guillaume Jaume, Lauren Alisha Fernandes +13
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue.…
Towards Explainable Graph Representations in Digital Pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez +6
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encod…
NINEPINS: Nuclei Instance Segmentation with Point Annotations
Ting-An Yen, Hung-Chun Hsu, Pushpak Pati +3
Deep learning-based methods are gaining traction in digital pathology, with an increasing number of publications and challenges that aim at easing the work of systematically and ex…