# Transfer Learning for Endoscopic Image Classification

Source: [Transfer Learning for Endoscopic Image Classification](<https://www.sota2.com/research/paper/68fa62ba1e209131ce9fae67>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:25:18.953Z

Authors: Shoji Sonoyama, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka

Published: 2016-08-24T04:51:53.000Z

## Abstract

In this paper we propose a method for transfer learning of endoscopic images. For transferring between features obtained from images taken by different (old and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed by Hoffman et al. in order to use L2 distance constraints as regularization, called Max-Margin Domain Transfer with L2 Distance Constraints (MMDTL2). Furthermore, we develop the dual formulation of the optimization problem in order to reduce the computation cost. Experimental results demonstrate that the proposed MMDTL2 outperforms MMDT for real data sets taken by different endoscopes.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1608.06713>)

[PDF](<https://arxiv.org/pdf/1608.06713.pdf>)