SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. In pathology images, there is another level of imbalance, where given a positively labeled Whole Slide Image (WSI), only a fraction of pixels within it contribute to the positive label. This compounds the severity of imbalance and makes imbalanced classification in pathology challenging. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings

A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis.

BACKGROUND The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm. METHODS NASH patients with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI; Boston, MA). Using trichrome-stained biopsies in the training set (n=130), an ML model was developed to recognize fibrosis patterns associated with HVPG and the resultant ML HVPG score was validated in a held-out test set (n=88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH, HVPG ≥10 mm Hg) were determined. RESULTS The ML HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ=0.47 vs ρ=0.28; p<0.001). The ML HVPG score differentiated patients with normal (0-5 mmHg) and elevated HVPG (5.5-9.5 mmHg), and CSPH (median: 1.51 vs 1.93 vs 2.60; all p<0.05). The AUROCs (95%CI) of the ML HVPG score for CSPH were 0.85 (0.80,0.90) and 0.76 (0.68,85) in the training and test sets, respectively. Discrimination of the ML HVPG score for CSPH improved with addition of a ML parameter for nodularity, ELF, platelets, AST, and bilirubin (AUROC in test set: 0.85;95%CI 0.78,0.92). While baseline ML HVPG score was not prognostic, changes were predictive of clinical events (HR 2.13; 95%CI 1.26,3.59) and associated with hemodynamic response and fibrosis improvement. CONCLUSIONS A ML-model based on trichrome-stained liver biopsy slides can predict CSPH in NASH patients with cirrhosis