48 citations · 56 across the 9 of their papers we have counts for
10 papers
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
Dhanesh Ramachandram
Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks, thou…
CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
Sneha Rao, Shaina Raza, Dhanesh Ramachandram
Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only parti…
From Features to Actions: Explainability in Traditional and Agentic AI Systems
Sindhuja Chaduvula, Jessee Ho, Kina Kim +6
Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed…
Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario
Dhanesh Ramachandram, Anne Loefler, Surain Roberts +5
Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learni…
Transparent AI: The Case for Interpretability and Explainability
Dhanesh Ramachandram, Himanshu Joshi, Judy Zhu +3
As artificial intelligence systems increasingly inform high-stakes decisions across sectors, transparency has become foundational to responsible and trustworthy AI implementation.…
Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks
Sara Ketabi, Dhanesh Ramachandram
Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health r…
CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
Dhanesh Ramachandram, Ananya Raval
Competing risks are crucial considerations in survival modelling, particularly in healthcare domains where patients may experience multiple distinct event types. We propose CRISP-N…
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Dhanesh Ramachandram, Michal Lisicki, Timothy J. Shields +2
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images. Deep…
LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network
Dhanesh Ramachandram, Terrance DeVries
We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Network architecture which is…
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
Terrance DeVries, Dhanesh Ramachandram
We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 ne…