arXiv subjects

Janine M. Lupo

Publications and source records attributed to Janine M. Lupo.

3 recordsLinked to original sources

arXiv2026

Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging

Evaluating vision-language models (VLMs) on medical images requires benchmarks that are clinically grounded, scalable, and controlled for evaluation confounds. Existing public benchmarks are limited in scale, manually annotated, or potentially leaked into VLM pretraining corpora. We present an automated agent-driven pipeline that generates multiple-choice VQA datasets directly from paired private radiology reports and 3D oncology imaging, producing two complementary question types: RADS-style questions deterministically derived from clinician-defined reporting schemas, and radiology report-derived questions generated by an LLM from radiologist findings and verified against the source report. Applied to four in-house cancer cohorts, the pipeline yields an instance-contamination-controlled benchmark without per-question human annotation. Zero-shot evaluation of six VLMs reveals no dominant model and substantial headroom across all cells. A blind ablation reveals that visual reliance is highly dataset-specific: liver Report-derived questions genuinely require the image, while Lung CT is essentially solvable without it - the leading closed model exceeds its sighted accuracy on Lung CT when blinded - indicating that even private clinical data does not guarantee a contamination-controlled read of visual capability. The pipeline is released as an open agent skill for in-house redeployment.

cs.CV↗

arXiv2019

Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning

Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on 3 healthy volunteers as well as animal (N=6) and prostate patient (N=1) experiments. Results: In animal validation experiment, the differences/errors (mean difference $\pm$ standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 $\pm$ 486 and 154 $\pm$ 512 ms for T1, and 5 $\pm$ 33 and 7 $\pm$ 41 ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within ~5 seconds. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1 and T2 weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2 weighted images, in conjunction with segmentation of gray matter, white matter and CSF.

physics.med-ph↗

arXiv2019

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.

eess.IV↗