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Talent Scout

R

Rishikesh Bhyri

State University of New York at Buffalo

B

Brian R Quaranto

State University of New York at Buffalo

P

Philip J Seger

State University of New York at Buffalo

K

Kaity Tung

State University of New York at Buffalo

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References (28)

[1]
Exploring Contextual Attribute Density in Referring Expression Counting
2025Zhicheng Wang, Zhiyu Pan et al.
[2]
Qwen2.5-VL Technical Report
2025Shuai Bai, Keqin Chen et al.
[3]
CountGD: Multi-Modal Open-World Counting
2024Niki Amini-Naieni, Tengda Han et al.
[4]
Referring Expression Counting
2024Siyang Dai, Jun Liu et al.
[5]
SEP: Self-Enhanced Prompt Tuning for Visual-Language Model
2024Hantao Yao, Rui Zhang et al.
[6]
DAVE – A Detect-and-Verify Paradigm for Low-Shot Counting
2024Jer Pelhan, A. Lukežič et al.
[7]
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
2024Yi-xin Huang, Hou-I Liu et al.
[8]
Single Domain Generalization for Crowd Counting
2024Zhuoxuan Peng, Shueng-Han Gary Chan
[9]
VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
2023Seunggu Kang, WonJun Moon et al.
[10]
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
2023Mingyue Guo, Li Yuan et al.
[11]
Chain-of-Look Prompting for Verb-centric Surgical Triplet Recognition in Endoscopic Videos
2023Nan Xi, Jingjing Meng et al.
[12]
Open Set Video HOI detection from Action-centric Chain-of-Look Prompting
2023Nan Xi, Jingjing Meng et al.
[13]
A Low-Shot Object Counting Network With Iterative Prototype Adaptation
2022Nikola Djukic, A. Lukežič et al.
[14]
Chain of Thought Prompting Elicits Reasoning in Large Language Models
2022Jason Wei, Xuezhi Wang et al.
[15]
Prefix-Tuning: Optimizing Continuous Prompts for Generation
2021Xiang Lisa Li, Percy Liang
[16]
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
2021Ze Liu, Yutong Lin et al.
[17]
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
2019Jacob Devlin, Ming-Wei Chang et al.
[18]
Microscopy cell counting and detection with fully convolutional regression networks
2018Weidi Xie, J. Noble et al.
[19]
Focal Loss for Dense Object Detection
2017Tsung-Yi Lin, Priya Goyal et al.
[20]
Counting in the Wild
2016C. Arteta, V. Lempitsky et al.

Showing 20 of 28 references

Founder's Pitch

"Automated high-density surgical instrument counting using visual chain reasoning."

Medical AIScore: 8View PDF ↗

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0-10 scale

High Potential

3/4 signals

7.5

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4/4 signals

10

Series A Potential

4/4 signals

10

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Why It Matters

This research addresses a critical challenge in surgical procedures—accurate counting of surgical instruments, which is vital for ensuring patient safety. By automating this task, it reduces manual errors and enhances operational efficiency in Operating Rooms.

Product Angle

Develop a software tool integrating the CoLSR framework for use in hospitals and surgical centers, allowing medical staff to track and count instruments via a mounted camera system or handheld device app.

Disruption

Currently, the counting process is manual, prone to human error. This solution automates and improves accuracy over manual methods and potentially replaces less effective automated counting solutions that do not handle dense environments well.

Product Opportunity

Surgical centers and hospitals could benefit from this tool which not only improves accuracy but also reduces time spent on manual counting, potentially saving significant OR costs. The market includes thousands of surgical units globally with strong incentives for patient safety and operational efficiency improvements.

Use Case Idea

Automate pre- and post-operative surgical instrument inventory checks to prevent retained surgical items, improving patient safety and reducing operation room time costs.

Science

The paper introduces Chain-of-Look, a new framework that employs a visual reasoning method inspired by human sequential counting, called a 'visual chain'. It guides the identification process along a continuous path, rather than treating object detection as unordered events. This visual trajectory is optimized through a neighboring loss function that ensures the plausibility of spatial arrangements. This innovative approach is shown to outperform existing methods, particularly in dense environments like surgery instruments laid during operations, achieving this advancement with their newly developed dataset, SurgCount-HD.

Method & Eval

The method was evaluated using a dataset of 1,464 high-density surgical instrument images. Experiments compared the proposed approach to existing SOTA methods, demonstrating superior accuracy. The introduction of a neighboring loss and visual chains significantly enhanced performance in densely packed scenes.

Caveats

The method may face challenges with different instrument types not well-represented in the dataset or varying light conditions. Ensuring integration with existing hospital systems and privacy concerns regarding operational room recording must also be considered.

Author Intelligence

Rishikesh Bhyri

State University of New York at Buffalo
rbhyri@buffalo.edu

Brian R Quaranto

State University of New York at Buffalo
brianqua@buffalo.edu

Philip J Seger

State University of New York at Buffalo
pseger@buffalo.edu

Kaity Tung

State University of New York at Buffalo
kaitytun@buffalo.edu

Brendan Fox

State University of New York at Buffalo
btfox@buffalo.edu

Gene Yang

State University of New York at Buffalo
geneyang@buffalo.edu

Steven D. Schwaitzberg

State University of New York at Buffalo
schwaitz@buffalo.edu

Junsong Yuan

State University of New York at Buffalo
jsyuan@buffalo.edu

Peter C W Kim

State University of New York at Buffalo
pckim@buffalo.edu

Nan Xi

State University of New York at Buffalo
nanxi@buffalo.edu