Loss-Based Attention for Deep Multiple Instance Learning
Authors: Xiaoshuang Shi, Fuyong Xing, Yuanpu Xie, Zizhao Zhang, Lei Cui, Lin Yang5742-5749
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple types of benchmark databases demonstrate that the proposed attention mechanism is a general, effective and efficient framework, which can achieve superior bag and image classification performance over other state-of-the-art MIL methods, with obtaining higher instance precision and recall than previous attention mechanisms. |
| Researcher Affiliation | Academia | 1University of Florida, Gainesville, FL, USA 2University of Colorado Denver, Denver, CO, USA 3Northwestern University, Xi an, China {xsshi2015, shampool, zizhaozhang}@ufl.edu, fuyong.xing@ucdenver.edu, cuilei1989@163.com, lin.yang@bme.ufl.edu |
| Pseudocode | No | The paper describes the method using mathematical equations and diagrams, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Source codes are available on https://github.com/xsshi2015/Loss-Attention. |
| Open Datasets | Yes | We evaluate the proposed method, referred to as Loss Attention, on multiple benchmark MIL datasets (MUSK1, MUSK2, FOX, TIGER and ELEPHANT), MNIST-based and CIFAR-10-based MIL datasets, CIFAR-10 and Tiny Image Net image databases. |
| Dataset Splits | Yes | Following (Ilse, Tomczak, and Welling 2018) (Wang et al. 2018), we adopt 10-fold-crossvalidation and repeat five times per experiment for MIL and histopathology datasets. For experiments on MNISTbags, we utilize a fixed division into training and test sets. We build training sets with 50, 100, 150 and 200 bags, respectively, and a test set containing 1,000 bags. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions models like LeNet5 and ResNet18, but does not provide specific software dependencies or library versions (e.g., Python, PyTorch/TensorFlow versions) that would be needed for reproducibility. |
| Experiment Setup | No | The details of architectures, the parameter λ, ramp-up function ω(m), optimizer and hyperparameters are shown in the supplemental material (Tables A2, A3 and A4). This information is not provided in the main text of the paper. |