# US 2026/0094271 A1 · Efficient aggregation method and system for multiple instance learning

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- Number: US 2026/0094271 A1
- Document ID: US20260094271A1
- Status: Published application
- Kind: A1
- Application number: 19/335,126
- Filed: 2025-09-22
- Published: 2026-04-02
- Earliest claimed: 2024-09-27
- Assignees: Tata Consultancy Services Limited
- Applicants: Tata Consultancy Services Limited
- Inventors: PAVAN KUMAR REDDY KANCHAM; JAYAVARDHANA RAMA GUBBI LAKSHMINARASIMHA; SHWET DENISH MAKADIYA
- Web page: https://patentgrep.com/patent/US20260094271A1

## CPC

- G06T 7/0014: {using an image reference approach}
- G06F 18/28: Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
- G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/771: Feature selection, e.g. selecting representative features from a multi-dimensional feature space
- G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods}
- G06V 10/7747: {Organisation of the process, e.g. bagging or boosting}
- G06V 10/82: using neural networks
- G06V 20/69: Microscopic objects, e.g. biological cells or cellular parts
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G06V 20/698: {Matching; Classification}
- G06T 2207/10056: Microscopic image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T 2207/30096: Tumor; Lesion
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Multiple Instance Learning (MIL) is a useful method for extracting information from Gigapixel images using weakly supervised learning. Traditionally, it was approached with the hypothesis of considering each instance as an independent and identically distributed entity. Recently, it has been suggested that correlations between different instances would yield better results for classification. The present disclosure is designed to improve classification performance significantly. The self-selective MIL of the present disclosure facilitates binary classification with enhanced insights into more aggressive tumor regions.

## Claims

### Claim 1

A processor-implemented method, the method comprising:
receiving, by one or more hardware processors, a plurality of Whole Slide Images (WSIs) corresponding to a plurality of subjects;
generating, by the one or more hardware processors, a plurality of non-overlapping patch instances from each of the plurality of WSIs, wherein a background associated with each of the plurality of non-overlapping patch instances were removed;
extracting, by the one or more hardware processors, a plurality of features from the plurality of patch instances associated with each of the plurality of WSI, using a Deep Neural Network (DNN);
computing, by the one or more hardware processors, a plurality of feature embeddings based on the extracted plurality of features using a Machine Learning (ML) model; and
training, by the one or more hardware processors, a transformed-based Multiple Instance Learning (MIL) model based on the plurality of feature embeddings associated with each of the plurality of patch instances by:
generating a first triple vector comprising a Query (Q), a Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings;
computing a first plurality of correlation scores between each feature embedding from among the plurality of feature embeddings with other plurality of feature embeddings from among the plurality of feature embeddings based on the first triple vector;
selecting a first optimal feature embedding from among the plurality of feature embeddings based on the first plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the optimal feature embedding;
generating a second triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the first optimal feature embedding;
computing a second plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the first optimal feature embedding based on the second triple vector;
selecting a second optimal feature embedding from among the plurality of feature embeddings based on the second plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the second optimal feature embedding;
generating a third triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the second optimal feature embedding;
computing a third plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the second optimal feature embedding based on the third triple vector; and
selecting a third optimal feature embedding from among the plurality of feature embeddings based on the third plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the third optimal feature embedding.

### Claim 2 (depends on 1)

The processor implemented method of claim 1, during inference, the trained transformed-based MIL model predicts the bag label on receiving the WSI.

### Claim 3 (depends on 1)

The processor implemented method of claim 1, wherein steps for selecting an optimal feature embedding from among the plurality of feature embeddings based on correlation scores comprises:
receiving the plurality of original feature embeddings associated with the WSI;
arranging the plurality of correlation scores in the descending order for the plurality of original feature embeddings;
choosing the top correlation scores among the plurality of correlation scores; and
selecting the first selected feature embeddings based on the chosen top correlation scores.

### Claim 4

A system comprising:
at least one memory storing programmed instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to:
receive a plurality of Whole Slide Images (WSIs) corresponding to a plurality of subjects;
generate a plurality of non-overlapping patch instances from each of the plurality of WSIs, wherein a background associated with each of the plurality of non-overlapping patch instances were removed;
extract a plurality of features from the plurality of patch instances associated with each of the plurality of WSI, using a Deep Neural Network (DNN);
compute a plurality of feature embeddings based on the extracted plurality of features using a Machine Learning (ML) model; and
train a transformed-based Multiple Instance Learning (MIL) model based on the plurality of feature embeddings associated with each of the plurality of patch instances by:
generating a first triple vector comprising a Query (Q), a Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings;
computing a first plurality of correlation scores between each feature embedding from among the plurality of feature embeddings with other plurality of feature embeddings from among the plurality of feature embeddings based on the first triple vector;
selecting a first optimal feature embedding from among the plurality of feature embeddings based on the first plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the optimal feature embedding;
generating a second triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the first optimal feature embedding;
computing a second plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the first optimal feature embedding based on the second triple vector;
selecting a second optimal feature embedding from among the plurality of feature embeddings based on the second plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the second optimal feature embedding;
generating a third triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the second optimal feature embedding;
computing a third plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the second optimal feature embedding based on the third triple vector; and
selecting a third optimal feature embedding from among the plurality of feature embeddings based on the third plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the third optimal feature embedding.

### Claim 5 (depends on 4)

The system of claim 4, during inference, the trained transformed-based MIL model predicts the bag label on receiving the WSI.

### Claim 6 (depends on 4)

The system of claim 4, wherein steps for selecting an optimal feature embedding from among the plurality of feature embeddings based on correlation scores comprises:
receiving the plurality of original feature embeddings associated with the WSI;
arranging the plurality of correlation scores in the descending order for the plurality of original feature embeddings;
choosing the top correlation scores among the plurality of correlation scores; and
selecting the first selected feature embeddings based on the chosen top correlation scores.

### Claim 7

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
receiving a plurality of Whole Slide Images (WSIs) corresponding to a plurality of subjects;
generating a plurality of non-overlapping patch instances from each of the plurality of WSIs, wherein a background associated with each of the plurality of non-overlapping patch instances were removed;
extracting a plurality of features from the plurality of patch instances associated with each of the plurality of WSI, using a Deep Neural Network (DNN);
computing a plurality of feature embeddings based on the extracted plurality of features using a Machine Learning (ML) model; and
training a transformed-based Multiple Instance Learning (MIL) model based on the plurality of feature embeddings associated with each of the plurality of patch instances by:
generating a first triple vector comprising a Query (Q), a Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings;
computing a first plurality of correlation scores between each feature embedding from among the plurality of feature embeddings with other plurality of feature embeddings from among the plurality of feature embeddings based on the first triple vector;
selecting a first optimal feature embedding from among the plurality of feature embeddings based on the first plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the optimal feature embedding;
generating a second triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the first optimal feature embedding;
computing a second plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the first optimal feature embedding based on the second triple vector;
selecting a second optimal feature embedding from among the plurality of feature embeddings based on the second plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the second optimal feature embedding;
generating a third triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the second optimal feature embedding;
computing a third plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the second optimal feature embedding based on the third triple vector; and
selecting a third optimal feature embedding from among the plurality of feature embeddings based on the third plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the third optimal feature embedding.

### Claim 8 (depends on 7)

The one or more non-transitory machine readable information storage mediums of claim 7, during inference, the trained transformed-based MIL model predicts the bag label on receiving the WSI.

### Claim 9 (depends on 7)

The one or more non-transitory machine readable information storage mediums of claim 7, wherein steps for selecting an optimal feature embedding from among the plurality of feature embeddings based on correlation scores comprises:
receiving the plurality of original feature embeddings associated with the WSI;
arranging the plurality of correlation scores in the descending order for the plurality of original feature embeddings;
choosing the top correlation scores among the plurality of correlation scores; and
selecting the first selected feature embeddings based on the chosen top correlation scores.