Learning General-purpose Biomedical Volume Representations using Randomized Synthesis
Authors: Neel Dey, Benjamin Billot, Hallee Wong, Clinton Wang, Mengwei Ren, Ellen Grant, Adrian Dalca, Polina Golland
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that the resulting features and weights enable broad generalization on the key biomedical tasks of 3D registration and segmentation across several diverse datasets. We achieve state-of-the-art unsupervised multimodality image registration by simply using the network s approximately appearance invariant and pose equivariant representations (Fig. 1) to drive existing registration solvers. The proposed network can also be used as an off-the-shelf dataset-agnostic initialization for finetuning on any voxel-level task. Specifically, we demonstrate strong few-shot segmentation performance across several highly variable downstream datasets, thereby removing the need for the cumbersome dataset-specific self-supervised pretraining commonly used today. |
| Researcher Affiliation | Academia | 1 MIT CSAIL 2 New York University 3 Harvard Medical School EMAIL |
| Pseudocode | Yes | Algorithm 1 3D synthetic label map L generation |
| Open Source Code | Yes | Code, tutorials, and model weights are available at https://www.neeldey.com/anatomix/. |
| Open Datasets | Yes | As templates, we use the freely available 45,000 binary volumes from the Total Segmentator dataset of 104 annotated organs in 1,204 CT volumes (Wasserthal et al., 2023). |
| Dataset Splits | Yes | we split L2RAb and MM-WHS into 1/7 and 5/15 validation/testing pairs, respectively, where the validation pair(s) are only used for tuning registration hyperparameters. [...] The dataset splits are in App. B.5. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions a "3T Skyra Siemens scanner" in the context of data acquisition for the WUFetal dataset, not for model training or inference. |
| Software Dependencies | Yes | Our data engine in Fig. 2 A & B has several components. Here, we describe low-level implementation details. We note that we make extensive use of the MONAI (Cardoso et al., 2022), Torch IO (P erez Garc ıa et al., 2021), and scikit-image (van der Walt et al., 2014) libraries for both label and volume synthesis. [...] Convex Adam is a high-performance multimodality registration solver and we use the b2671f8 commit of the repository4. |
| Experiment Setup | Yes | F and Z are pretrained jointly for 600,000 iterations with a batch size of one 1283 label map, each generating two 1283 volumes. We compute the contrastive loss (with temperature τ = 0.33) on 512 randomly sampled indices at each iteration for each decoder layer due to memory limitations. [...] we finetune all layers of each network for a high number of iterations (37,500) with cosine learning rate decay. |