Learning Causal Alignment for Reliable Disease Diagnosis
Authors: Mingzhou Liu, Ching-Wen Lee, Xinwei Sun, Xueqing Yu, YU QIAO, Yizhou Wang
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method on two medical diagnosis applications, showcasing faithful alignment to radiologists. Code is publicly available at https://github.com/lmz123321/Causal_alignment. [...] In this section, we evaluate our method on two medical diagnosis tasks: the benign/malignant classification of lung nodules and breast masses. [...] We repeat 3 different seeds to remove the effect of randomness. [...] Table 1: Comparison with baseline methods on LIDC-IDRI and CBIS-DDSM datasets. [...] Table 2: Ablation study on LIDC-IDRI and CBIS-DDSM datasets. [...] Figure 4: CAM visualization. Each row denotes different cases. |
| Researcher Affiliation | Academia | Mingzhou Liu1 Ching-Wen Lee1 Xinwei Sun 2 Xueqing Yu1 Yu Qiao 3 Yizhou Wang4,1,5,6,7 1 School of Computer Science, Peking University 2 School of Data Science, Fudan University 3 School of Automation and Intelligent Sensing, Shanghai Jiao Tong University 4 Center on Frontiers of Computing Studies, Peking University 5 Institute for Artificial Intelligence, Peking University 6 Nat l Eng. Research Center of Visual Technology, Peking University 7 State Key Lab. of General Artificial Intelligence, Peking University Corresponding authors EMAIL EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Causal alignment training Input: Data D, Output: Decision model fθ, Hyperparameters: Sparsity regularization α, weight of alignment loss λ, learning rate η. 1: while not converged do 2: **Forward pass 3: Compute Lce. 4: Optimize (2) to obtain x and compute Lalign using (3). 5: Compute L Lce + λLalign. 6: **Back propagation 7: Estimate θLalign with conjugate gradient. 8: Update θ: θ θ η θL. // or Adam 9: end while |
| Open Source Code | Yes | Code is publicly available at https://github.com/lmz123321/Causal_alignment. |
| Open Datasets | Yes | We consider the LIDC-IDRI dataset Armato III et al. (2011) for lung nodule classification and the CBIS-DDSM dataset Lee et al. (2017) for breast mass classification. |
| Dataset Splits | Yes | We split the dataset into training (n = 731), validation (n = 238), and test (n = 244) sets. The CBIS-DDSM dataset ... We follow the official dataset split, with 691 masses in the training set and 200 masses in the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | We use the Adam optimizer and set the learning rate as 0.001. We adopt the Torch Opt Ren et al. (2022) package to implement the conjugate gradient estimator. |
| Experiment Setup | Yes | We use the Adam optimizer and set the learning rate as 0.001. We parameterize the attributes prediction network fθ1 with a seven-layer Convolutional Neural Network (CNN), and train it for 100 epochs with a batch size of 128 for each iteration. For the classification network fθ2, we parameterize it with a two-layer Multi-Layer Perceptron (MLP), and train it for 30 epochs with a batch size of 128. Please refer to Appx. B for details of the network architectures. For the hyperparameters α1 in (7) and α2 in (6), we set them to α1 = 0.01, α2 = 0.0005 for LIDC-IDRI and α1 = 0.07, α2 = 0.0005 for CBIS-DDSM, respectively. For both datasets, we set λ1 = λ2 = 1 in (5). |