Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward

Author:

Mathur Archana1,Arya Nikhilanand2,Pasupa Kitsuchart3,Saha Sriparna4,Roy Dey Sudeepa5,Saha Snehanshu67

Affiliation:

1. Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology , Yelahanka, 560064, Karnataka , India

2. School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University , Bhubaneshwar, 751024, Odisha , India

3. School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang , 1 Soi Chalongkrung 1, 10520, Bangkok , Thailand

4. Computer Science and Engineering, Indian Institute of Technology Patna , Bihta, 801106, Bihar , India

5. Department of Computer Science and Engineering, PES University , Hosur Road, 560100, Karnataka , India

6. CSIS and APPCAIR, BITS Pilani K.K Birla Goa Campus , Goa, 403726, Goa , India

7. Div of AI Research, HappyMonk AI , Bangalore, 560078, Karnataka , India

Abstract

Abstract We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.

Funder

BITS Pilani K K Birla Goa Campus

SERB SURE-DST

SERB CRG- DST

Publisher

Oxford University Press (OUP)

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