Multistream fusion segmentation and classification of prostate lesions from magnetic resonance images
Bibliographic record
Abstract
Prostate cancer is a significant contributor to cancer-related deaths in men. Detecting prostate cancer early can greatly increase the likelihood of successful treatment. However, detecting and assessing prostate lesions from multiparametric magnetic resonance images (MRI) is time-consuming and variable across radiologists with different levels of experience. We present an integrated framework for segmenting and classifying prostate lesions from MRI. The proposed approach is in contrast with most existing automated prostate analysis approaches, which treat segmentation and classification of prostate lesions as two separate tasks with no interactions between them. In the proposed framework, preliminary lesion boundaries were first segmented from T2-weighted (T2W) and diffusion-weighted images (DWI) by a three-stream network. The region of interest (ROI) enclosing the segmented lesion was fed to a weakly supervised classification network, which predicted the Gleason grade of the lesion and provided the class activation maps (CAMs) corresponding to multiple MRI modalities. Finally, MR images of different modalities with the corresponding CAMs were fed to a six-stream network to generate an enhanced lesion mask. Our experiments showed that CAMs generated by the proposed weakly supervised classifier improved segmentation performance. Our proposed method has a great potential to improve the accuracy and efficiency of prostate MRI interpretation workflow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".