6 papers
Fixing a Model That Learned Worse Cancer Means Lower Risk: Monotonic Constraints in Bladder Cancer Recurrence Prediction
Saram Abbas, David Thomas, Naeem Soomro +3
Background and Objective: Clinicians expect recurrence risk to climb with cancer severity. In a UK multicentre trial, an unconstrained XGBoost model learnt that higher tumour stage…
Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction
Ayusha Abbas, Saram Abbas, Kabita Adhikari
Anomaly detectors are hardest to deploy exactly where training data is scarcest: a newly commissioned production line has a handful of verified "golden" samples and no machine-lear…
Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation
Ayusha Abbas, Saram Abbas, Kabita Adhikari
Prostate cancer claims a life every 80 seconds. Early detection is needed to prevent disease progression, and both PSA density calculation and biopsy decisions rely on knowing the…
AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines
Saram Abbas, Naeem Soomro, Rishad Shafik +2
Bladder cancer claims one life every 3 minutes worldwide. Most patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC), yet up to 70% recur after treatment, triggeri…
Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction
Saram Abbas, Naeem Soomro, Rishad Shafik +2
Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence rates soaring as high as 70-80%. Each recurrence triggers a cascade of invasive pr…
Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction
Saram Abbas, Rishad Shafik, Naeem Soomro +2
Notorious for its 70-80% recurrence rate, Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools…