arXiv subjects

Soheil Kooklan

Publications and source records attributed to Soheil Kooklan.

2 recordsLinked to original sources

arXiv2026

Report-Derived Labels, an Image Teacher and the Gap to Expert Labels in Knee MRI: An Analysis of One Entry to the RSNA 2026 Knee Abnormality Detection Challenge

In the RSNA 2026 Knee Abnormality Detection challenge, 58 of 4,407 training studies have expert labels for 12 findings, and the rest have only a radiology report. We compare training targets built from a language-model reading of the reports and from a public image model against two references: the expert labels and the 39,248 study-finding cells that the reports state as present or absent. The report labels and the out-of-fold predictions of the image model reach similar agreement with the expert labels (macro AUC 0.892 and 0.896) but fail on different findings. The report is better for ligament tears and patellofemoral osteoarthritis, which it never calls present when the experts do not; the image model is better for effusion, Baker's cyst, contusion, and synovitis, where the reports call the finding absent, leave it out, or call it present more often than the experts. Their equal mixture reaches 0.927 (gain over the report labels 0.035, 95% interval 0.019 to 0.052), also after removing the studies whose knee side was handled inconsistently. We did not observe a model-level gain from this better target: a 2.5D ResNet50 trained on the report labels alone scored 0.877 on the private leaderboard, after the deadline, against 0.872 for the same model trained on the mixture, and the out-of-fold advantage of the mixture is confounded by a change of code and by possible leakage through the image model's folds. A scanner prevalence prior raised agreement with the reports but not with the experts, so a report-derived reference can reward reporting habits. A rank correction for co-occurring findings gave small out-of-fold gains and no visible leaderboard effect. Our entry, a forked public ensemble with that correction, was displayed at rank 582 of 6,299 on the private leaderboard (0.943, a score shared by 287 teams). All results are exploratory; none of the models is meant for clinical use.

eess.IV↗

arXiv2026

An Error Budget for Shuffled Multimodal Windows in the 3rd WEAR Challenge: Stacking, Count Constraints and Seam Matching

The 3rd WEAR Dataset Challenge asks for an activity label for every one-second window of four unseen participants. Each window has 15 VideoMAEv2 frame embeddings, the accelerometer signal of one random limb, and the windows are shuffled. This report is an empirical analysis of one system built after the challenge deadline. Window classifiers are stacked with features averaged over per-participant graphs of video neighbours and scored successor candidates, and a mean-field style decoder with a soft count constraint per activity labels all windows of a participant together. The setting is offline and transductive, and the count constraint relies on each activity being performed once. In a participant-wise simulation with participant-level bootstrap intervals, stacking added 0.051 macro F1 over stage-1 fusion and the count constraint 0.011 (95% interval 0.006 to 0.017). A quadratic seam distance ranks the true same-limb successor first among about 720 candidates in 83.9% of cases, but adding it to the link model changed macro F1 by -0.0001 (-0.0006 to 0.0005). With the decoder settings and the mixing weight chosen inside the folds and the mislabelled spans left out of training, the system scores 0.936 in simulation (0.940 for participants who performed each activity once); the submitted configuration scores 0.938 (0.943). Per-participant scores range from 0.80 to 0.97. These results come from a non-nested stacking protocol that uses unlabelled features transductively, so they are not a fully inductive estimate. An oracle that averages the final unary scores over the true segments reaches 0.973, and 79% of the remaining errors involve the null class. On the private leaderboard, which no choice used, the selected Kaggle submission scored 0.9231 (public 0.9254, with bounds partly tuned there), and a later one with bounds chosen in simulation scored 0.9247 (public 0.9253).

cs.CV↗