Skip to content
Rishabh Hota

Research & publications

Papers, reviewed and in review.

Medical-imaging segmentation, quantum self-attention for Vision Transformers, and clinical decision support.
  1. [1]

    LCSNet: Lightweight Caries Segmentation Network for dental caries segmentation from smartphone photographs

    • Built a dataset of smartphone photos of teeth with varying resolution and lighting.
    • Proposed LCSNet, a new lightweight segmentation architecture for dental caries. It reached an F1-score of 71.7% and IoU of 56.8% with significantly fewer parameters and FLOPs than heavier baselines.
    • Results were validated by expert dentists.
    • Published October 9, 2025.
    ● Published

    Intelligence-Based Medicine (Elsevier, Impact Factor 2.3)Oct 2025

    Read paper ↗
  2. [2]

    QFPViT: A Fidelity-Phase Quantum Self-Attention Operator for Vision Transformers — Design and Evaluation on MedMNIST

    • Designs a fidelity-phase quantum self-attention operator for Vision Transformers and evaluates it on MedMNIST medical-imaging benchmarks.
    • Rishabh started with no quantum-computing background, with the goal of building a better attention mechanism using quantum algorithms. QFPViT is the result.
    • Communicated to the journal Quantum.
    ○ Under review

    Quantum (journal)

  3. [3]

    Do Quantum-Inspired Feature Maps Help Vision Transformer Attention? A Matched-Budget Dequantization Study

    • Tests whether quantum-inspired feature maps actually improve Vision Transformer attention when compared at a matched compute and parameter budget. This dequantization study separates real gains from the effect of simply using more resources.
    • Communicated to Quantum Machine Intelligence (Springer).
    ○ Under review

    Quantum Machine Intelligence (Springer)

  4. [4]

    Depression Identification Decision Support System (DIDSS)

    Hota R.K., Verma N.

    • A decision support system for identifying depression, published as a chapter in Springer's Advanced Machine Learning for Real World Applications.
    • Authors: Hota R.K. and Verma N.
    ● Published

    Springer — Advanced Machine Learning for Real World Applications (book chapter)

    Read paper ↗
  5. [5]

    COVID-19 Classification with Chest CT

    Hota R.K., Akhilesh S.

    • Deep-learning classification of COVID-19 from chest CT scans.
    • Authors: Hota R.K. and Akhilesh S.
    ○ Submitted

    International Journal of Computing Science & Mathematics (Inderscience; Scopus & ESCI)

    Read paper ↗
  6. [6]

    Multi-Modal Decision Support for COVID-19 Prediction

    • A multi-modal decision support approach that combines vitals, symptoms and imaging to predict COVID-19, building on his COVID-19 Decision Support System project.
    • Communicated to Inderscience.
    ○ Under review

    Inderscience

  7. [7]

    OA Classification and Reporting using Agentic AI and Deep Neural Networks

    • Ongoing research with OsteosConnect. It extends his osteoarthritis X-ray classification work with agentic AI that generates clinical reports.
    ○ In progress

    With OsteosConnect