# Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks

arXiv: https://arxiv.org/abs/2304.13192 · HTML: https://mlsanity.com/paper/2304.13192/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2002.06262/) (2020)
- [Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification](https://mlsanity.com/paper/2009.04057/) (2020)
- [A Comparative Study on Polyp Classification using Convolutional Neural Networks](https://mlsanity.com/paper/2007.06071/) (2020)
- [HySenSe: A Hyper-Sensitive and High-Fidelity Vision-Based Tactile Sensor](https://mlsanity.com/paper/2211.04571/) (2022)
- [Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing](https://mlsanity.com/paper/2211.04573/) (2022)
- [Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks](https://mlsanity.com/paper/2211.06814/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor](https://mlsanity.com/paper/2310.08398/) (2023)
- [Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging](https://mlsanity.com/paper/2408.01554/) (2024)

## Abstract (arXiv)

In this study, toward addressing the over-confident outputs of existing artificial intelligence-based colorectal cancer (CRC) polyp classification techniques, we propose a confidence-calibrated residual neural network. Utilizing a novel vision-based tactile sensing (VS-TS) system and unique CRC polyp phantoms, we demonstrate that traditional metrics such as accuracy and precision are not sufficient to encapsulate model performance for handling a sensitive CRC polyp diagnosis. To this end, we develop a residual neural network classifier and address its over-confident outputs for CRC polyps classification via the post-processing method of temperature scaling. To evaluate the proposed method, we introduce noise and blur to the obtained textural images of the VS-TS and test the model's reliability for non-ideal inputs through reliability diagrams and other statistical metrics.