Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks
Authors: Hao Chen, Qi Dou, Xi Wang, Jing Qin, Pheng Heng
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed. |
| Researcher Affiliation | Academia | Hao Chen , Qi Dou , Xi Wang , Jing Qin , Pheng-Ann Heng , Department of Computer Science and Engineering, The Chinese University of Hong Kong College of Computer Science, Sichuan University, China School of Medicine, Shenzhen University, China Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | The datasets were obtained from the 2012 and 2014 ICPR MITOSIS contests1. 1More details: http://ipal.cnrs.fr/ICPR2012/, http://mitosatypia-14.grand-challenge.org/ |
| Dataset Splits | Yes | For each dataset, we split training data with ground truth into two sets for training and validation (about 1/7 of total training data), respectively. |
| Hardware Specification | Yes | Our system was implemented with the mixed programming of MATLAB and C++. The coarse retrieval model took about 0.45 seconds to process per 4Mpixels HPF (size 2084 2084) and the fine discrimination model with 10 input variations cost about 0.49 seconds using a workstation with a 2.50 GHz Intel(R) Xeon(R) E5-2609 CPU and a NVIDIA Ge Force GTX TITAN GPU. |
| Software Dependencies | No | The paper mentions "MATLAB and C++" but does not specify version numbers for C++ or any other software libraries or frameworks used with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific optimizer settings. |