| 講演抄録/キーワード |
| 講演名 |
2021-03-04 16:35
Quantifying detection quality in the presence of adversarial inputs in dermatological images ○Mishra Sourav(UTokyo)・Hideaki Imaizumi(exMedio)・Toshihiko Yamasaki(UTokyo) PRMU2020-82 |
| 抄録 |
(和) |
(まだ登録されていません) |
| (英) |
We have tested deep learning based detection on dermatological conditions commonly encountered in clinical settings. Despite successes in diagnosing critical and morbid conditions such as Melanoma, it is not well understood if such models can reduce the patient burden on doctors by screening benign diseases. Most projects traditionally use pristine data acquired in controlled conditions. This may not reflect regular clinical workflows where image quality is non-ideal. We test the performance of deep learning methods on such data by simulating imperfections on user-submitted images of common disease labels. In our study, we have found the overall predictions change significantly despite robust training, contraindicating the maturity to enter mainstream medical diagnostics. |
| キーワード |
(和) |
/ / / / / / / |
| (英) |
deep learning / dermatology / / / / / / |
| 文献情報 |
信学技報, vol. 120, no. 409, PRMU2020-82, pp. 77-82, 2021年3月. |
| 資料番号 |
PRMU2020-82 |
| 発行日 |
2021-02-25 (PRMU) |
| ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
| PDFダウンロード |
PRMU2020-82 |