arXiv subjects

Anirudh Choudhary

Publications and source records attributed to Anirudh Choudhary.

3 recordsLinked to original sources

arXiv2026

RACR-MIL: Rank-aware contextual reasoning for weakly supervised grading of squamous cell carcinoma using whole slide images

Squamous cell carcinoma (SCC) is one of the most common cancer subtype, with an increasing incidence and a significant impact on cancer-related mortality. SCC grading using whole slide images is inherently challenging due to the lack of a standardized grading protocol and substantial tissue heterogeneity. We propose RACR-MIL, a weakly-supervised SCC grading approach that achieves robust generalization across multiple anatomies (skin, head & neck, lung). RACR-MIL is an attention-based multiple-instance learning framework that introduces two key innovations for learning grade-specific contextual representations: (1) a hybrid WSI graph that captures both local tissue context and non-local phenotypic dependencies between tumor regions, and (2) rank-ordering constraints on the attention mechanism that encourage consistent prioritization of higher-grade tumor regions and improve region-level grade confidence, aligning with pathologist's diagnostic process. Our model achieves state-of-the-art performance across multiple SCC datasets, achieving 3-9% improvements over existing methods and up to 10% improvement in tumor localization. In a pilot study, pathologists reported that RACR-MIL improved grading efficiency in 60% of cases, underscoring its potential as a clinically viable cancer diagnosis and grading assistant.

cs.CV↗

arXiv2025

Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, and early detection remains a major clinical challenge due to the absence of specific symptoms and reliable biomarkers. In this work, we propose a new multimodal approach that integrates longitudinal diagnosis code histories and routinely collected laboratory measurements from electronic health records to detect PDAC up to one year prior to clinical diagnosis. Our method combines neural controlled differential equations to model irregular lab time series, pretrained language models and recurrent networks to learn diagnosis code trajectory representations, and cross-attention mechanisms to capture interactions between the two modalities. We develop and evaluate our approach on a real-world dataset of nearly 4,700 patients and achieve significant improvements in AUC ranging from 6.5% to 15.5% over state-of-the-art methods. Furthermore, our model identifies diagnosis codes and laboratory panels associated with elevated PDAC risk, including both established and new biomarkers. Our code is available at https://github.com/MosbahAouad/EarlyPDAC-MML.

cs.LG↗

arXiv2021

Reinforcement Learning based Disease Progression Model for Alzheimer's Disease

We model Alzheimer's disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) and the trained RL agent to predict individualized 10-year AD progression using baseline (year 0) features on synthetic and real data. The model was comparable or better at predicting 10-year cognition trajectories than state-of-the-art learning-based models. Our interpretable model demonstrated, and provided insights into, "recovery/compensatory" processes that mitigate the effect of AD, even though those processes were not explicitly encoded in the model. Our framework combines DEs with RL for modelling AD progression and has broad applicability for understanding other neurological disorders.

cs.LG↗